Authors in [30] conducted a study on breast cancer classification using traditional machine learning (ML) and deep learning (DL) techniques. They achieved accuracy ranging from 93.25% to 93.97% by extracting features based on color histograms and Haralick textures. In [31] , the authors proposed using machine learning models for breast cancer classification tasks, achieving an accuracy of 91% compared with the average accuracy of approximately 79% achieved by physicians. In [32] , researchers utilized logistic regression, SVM, K-Nearest neighbor, and artificial neural networks for breast cancer image classification, achieving an average accuracy of 99.3%. The authors in [33] achieved a classification accuracy of 93.29% for skin cancer classification using deep learning techniques, whereas researchers in [34] used a DCNN to classify burnt human skin images, achieving an accuracy of 83.4%. Mahmood et al. [35] proposed a deep-learning approach to automatically detect and classify breast cancer using six DCNN models, with the ResNet50 model achieving the highest accuracy of 98.5% in detecting breast cancer, and the VGG16 model achieved the highest accuracy of 96.4% in classifying cancer types. In [36] , the authors conducted an extensive study on classifying breast cancer images using deep learning and imaging techniques, analyzing 49 research works and outlining ten open research challenges to guide new researchers in the field. Mahmood et al. [37] proposed six deep learning-based methods for the early detection of breast cancer masses and calcification clusters using mammography, achieving a classification accuracy of 97.8%. The proposed model is compatible with conventional pathological practices and can reduce the workload of logists by analyzing mammography images. Existing research on breast cancer classification has shown impressive accuracy using various deep learning models, but these studies often lack interpretability and transparency in their AI models. Methods relying on specific feature extractions, such as color histograms and Haralick textures, may not generalize well across different datasets. Additionally, while high accuracy rates have been achieved, the black-box nature of these models limits their practical application in clinical settings. Our study aims to overcome these limitations by integrating Deep Learning with Grad-CAM, enhancing both the accuracy and interpretability of breast cancer detection. This approach provides clearer insights into model decisions, fostering greater trust and reliability in AI-driven diagnostics.

A number of studies in the recent past have tried to evaluate the effectiveness of Grad-CAM for visualizing regions of interest delineated by deep learning models trained on medical image analysis in the breast cancer detection and diagnosis using the MIAS Mammographic Image Analysis Society. Chang et al. (2021) recently showed that Grad-CAM is effective in highlighting the optic nerve head and peripapillary nerve fiber bundles when weakly trained in limited training datasets, to improve the interpretability and generalizability of their model within the purview of glaucoma detection. Billah et al. (2022) used Grad-CAM in identifying the crucial characteristics responsible for cancer development in breast cancer histology images. The work shows that Grad-CAM could effectively visualize malignancy-related regions, leading to a guide on the model’s decision process that could give potential utility in cancer diagnostic tasks. The released posters have indicated the relevancy of applying Grad-CAM-based model suggestions to multimodal deep learning to combine mammography and ultrasound images for the purpose of breast cancer classification and localization. They applied Grad-CAM for the visualization of RIO for both modalities, leading to better model prediction understanding and potentially enhancing the diagnostic accuracy for this decision.

Sharma et al. (2023) [9] researched the application of Grad-CAM to explain the inference made by deep learning models on the classification of mammographic masses and architectural distortions. They showed that Grad-CAM could highlight the salient regions in mammograms, hence giving a possible explanation of the model’s decision process and, at the same time, reducing false positivity. Kim et al. (2023) [38] compared a set of explainable AI methods, including Grad-CAM, to interpret deep learning models in diagnosing breast cancer. The present study discusses the effectiveness of identifying methodology for acquiring insights into the model’s predictions and potential use in clinical settings. These are papers that show the Grad-CAM robustness and an immense opportunity for application in the field of medical image analysis, especially detection and diagnosis in breast cancer. Explanation of the model decisions and importance of highlighting important regions is achieved. Grad-CAM thus assists imperatively in model interpretation towards applications like diagnostic improvement and ultimately patient care. This study builds on the existing body of work on Grad-CAM by exploring its application in a novel context and proving its effectiveness in another domain. Therefore, the findings from this study will enhance the knowledge of the potential benefits and also possible limitations of Grad-CAM as a tool for explainable AI in medical imaging analysis. The recent studies summarized in Table 1 illustrate how Grad-CAM and deep learning have been applied in similar contexts, providing the comparative background for our proposed method.

Medical image processing in deep learning is one of the few fields that can, with high certainty, be considered revolutionary and has enhanced to a larger extent the possibilities of increasing diagnostic accuracy, especially in mammography [39] . The traditional Computer Aided-Detection/Diagnosis (CAD) systems, beneficial as they are, face a few limits in the process of feature selection and adaptability. Recent studies by Kim et al. (2018) [40] and Nica et al. (2021) [9] have shown that there is potential for such models to overcome these shortfalls through integration of advanced machine learning techniques. Medical imaging has witnessed a revolution in the recent years with the appliance of deep learning and deep model architectures in Convolutional Neural Networks. Their capability to detect subtle patterns from the imaging data makes them ideal in tasks like breast cancer detection [41] ; Kooi and Karssemeijer, 2017) [42] . However, the interpretability of the decision-making processes of DL models is a critical concern with respect to clinical applications. All the same, the main limitation of existing Computer-Aided Detection/Diagnosis systems, especially such as the feature selection and adaptability, the gap that this study aimed to fill in, is merged by the use of Grad-CAM. Grad-CAM gives visual explanations to the decisions of the CNN, highlighting the most influential regions in the images for the model prediction [43] [44] . Still, due to great leaps and bounds, the interpretability and adaptability of the models built on deep learning for imaging hold existing studies in significant limitation. Whereas traditional CAD systems form the basis of such studies, they often falter at optimal feature selection and adaptability across various datasets. This has been a matter of concern in black-box nature deep learning models for clinical application. Recent studies have shown that advanced machine learning techniques can outperform human-powered precision in the solving of medical problems. This study uses state-of-the-art DL models like MobileNet, Xception, and DenseNet for the classification of breast cancer from mammograms. The selection of the models was made based on performances demonstrated on image classifications, as noted from the studies by Li et al. 2019 [45] . This research will advance diagnostic precision in medical imaging and artificial intelligence and give deeper insight into AI-based decision-making within the scope of breast cancer diagnostics, which has been a major gap highlighted in recent studies. The key points are as follows:

Breast cancer clinical diagnosis is an important field that seems to be making paramount steps. Still, it suffers from limitations that dim the positive outcome of patients. The techniques performed drastically for early detection of breast cancer even though in many cases, the techniques fail to perform as expected due to a limit in the tools or techniques associated with it. Limits of such traditional CAD systems are prominently associated with issues in feature selection and adaptability, either of which can lead to inconsistent diagnostic performance across diverse patient populations and imaging conditions. Viewing this issue, the incorporation into clinical use is further complicated by the black-box nature of the deep learning models. Even though CNNs and other deep learning technologies have achieved high sensitivity with high accuracy in the detection of breast cancer, their operation is normally opaque, a character that makes it hard for clinicians to trust and easily interpret its outputs. In clinical settings, this lack of transparency has proved to be the most significant obstacle to widespread deployment of the technologies.

Moreover, existing research has largely focused on improving diagnostic accuracy without placing equal emphasis on model interpretability and transparency. Studies that use specific feature extraction methods, such as color histograms and Haralick textures, often face challenges in generalizing across different datasets, resulting in variability in diagnostic outcomes. Additionally, there is a critical need for cost-effective diagnostic technologies that can be deployed in resource-limited settings to ensure equitable access to healthcare. These inequalities are made worse by the expensive prices and technical requirements of modern imaging methods like MRI.

Personalised diagnostic methods that take into account the genetic and molecular features of certain tumours are also lacking in the present state of the art of research. In order to create individualised treatment programs and enhance patient results, such specialised tactics are crucial. Furthermore, less intrusive diagnostic procedures that put patient safety and comfort first without sacrificing diagnostic accuracy are critically needed.

In order to tackle these complex issues, our research improves the accuracy and transparency of breast cancer diagnosis models by combining transfer learning with Gradient-weighted Class Activation Mapping (Grad-CAM). In order to guarantee consistent performance across different datasets, transfer learning enhances model generalisation, while Grad-CAM offers visual explanations that emphasise the areas that have the greatest impact on model predictions. In addition to improving the diagnostic accuracy, this two-pronged strategy makes the decision-making process more visible, which in turn boosts confidence and dependability in AI-driven diagnostics. By bridging these critical gaps, our study aims to set a new standard in breast cancer diagnosis, ensuring that advanced AI technologies are both effective and interpretable, ultimately supporting better patient outcomes and more equitable healthcare solutions.

The Mammographic Image Analysis Society (MIAS) database of digital mammograms (v1.21) [50] consists of 322 high-resolution digital mammogram images (161 pairs), especially selected for breast cancer research and detection. These images were in a Portable Gray Map (PGM) file format, which is highly used to represent grayscale images. This dataset will facilitate developing, testing, and improving machine-learning algorithms in the analysis of mammograms and contains ground truth information per image. Members of the Mammographic Image Analysis Society partially contributed a small amount of contradiction to the small remainder altruistically and without financial benefit. This work is supported from internally generated grant funding from the organization (RNAG/302). Creative Commons license, which allows others to distribute, remix, adapt, or otherwise build on this dataset, as long as the work is credited back to the authors. Additionally, the dataset incorporates the original project’s study paper in PDF format alongside the mammogram images and a comprehensive README file, augmenting its utility. This collection serves as an invaluable asset for data scientists, academics, and medical professionals engaged in computer-aided diagnostics, mammography, and breast cancer detection [51] . The availability of high-quality images and meticulous ground truth data facilitates the development and enhancement of machine learning algorithms, contributing to advancements in the early identification and diagnosis of breast cancer.

Preprocessing is crucial in medical image analysis as it significantly impacts the accuracy and performance of the models used [52] . Proper execution of preprocessing steps is vital to avoid introducing artifacts or distortions that could compromise diagnostic accuracy. In our work, we use a publicly available preprocessed dataset and critically evaluate the preprocessing techniques applied to ensure they meet our analytical goals. This includes assessing data quality and confirming that no preprocessing-induced artifacts or errors are present. Key preprocessing steps involved are normalization, Image preprocessing techniques such as noise reduction and contrast enhancement play a vital role in improving image clarity and quality for subsequent analysis.

1) Normalization: This technique adjusts pixel intensities to a common range, enhancing the efficiency and stability of model training. Mathematically, normalization is expressed as:

$I_{norm}=\frac{I-\mu }{\sigma }$ (1)

where $I$ is the original image, $I_{norm}$ is the normalized image, $\mu$ is the mean intensity value, and $\sigma$ is the standard deviation.

2) Noise Reduction: In order to decrease picture noise, methods like median filtering and Gaussian blurring are used. Here is an instance where Gaussian noise reduction has been used:

$G\left(x,y\right)=\frac{1}{2\pi {\sigma }^2}{\text{e}}^{-\frac{x^2+y^2}{2{\sigma }^2}}$ (2)

where the standard deviation of the Gaussian distribution is denoted by $\sigma$ and the Gaussian function is represented by $G\left(x,y\right)$.

3) Contrast Enhancement: Histogram equalisation and other contrast enhancement techniques make medical picture features more visible. The formula for histogram equalisation is:

$H_{eq}\left(v\right)=\underset{j=0}{\overset{v}{{\displaystyle \sum }}}\frac{n_j}{N}$ (3)

where $H_{eq}\left(v\right)$ is the equalized histogram, $n_j$ is the number of pixels with intensity $j$, and $N$ is It is essential to tailor these preprocessing techniques to the specific characteristics and needs of the dataset to optimize the performance of deep learning models used for analysis.

the total number of pixels.

Creating a sophisticated and accurate classification system for detecting breast cancer (BC) in mammography pictures is the main objective of this work. Xception [53] , DenseNet [54] , and MobileNet [55] were part of the suite of deep learning (DL) models we used to achieve this. To improve picture categorisation accuracy, each model was hand-picked for its unique set of capabilities.

In this research, we look at how MobileNet—a mobile and embedded vision optimisation tool—can be used to identify breast cancer in mammograms. Achieving excellent performance while reducing computing needs and model size is the goal of MobileNet’s design. In order to improve diagnosis accuracy, we used a transfer learning strategy to tweak the model such that it was more sensitive to small breast cancer markers in mammography pictures.

By integrating MobileNet into the transfer learning paradigm, this study supports efficient case triage and flags cases requiring urgent treatment, which enhances patient care and healthcare system efficiency. Transfer learning with MobileNet allows exploration of various architectural and training strategies to strengthen diagnostic accuracy. Moreover, on-device processing with MobileNet ensures data privacy, as sensitive medical images remain confidential throughout the analysis process.

This study highlights the transformative potential of MobileNet and the transfer learning paradigm in breast cancer diagnosis based on mammogram images. By adapting MobileNet’s final layer and leveraging its computational efficiency, precision, and adaptability, this approach significantly enhances diagnostic accuracy and improves patient care outcomes.

This study is based on diagnosing breast cancer specifically, and the approach here presents the use of Xception, an advanced version of the Inception architecture. Xception is an adept, versatile tool that can be used in image analysis to better diagnostic precision and efficiency within the context of the mammograms. Xception will allow fine-tuning its layers using transfer learning, hence making it possible to identify the very soft notes of breast cancer in these images. The impact here is that it has brought a huge difference in the care and management of breast cancer. Being embedded into the transfer-learning paradigm is due to the seamless transition to the much less-dependant-on-massive-data-transfer mass, which means the very onerous case-based triage. Fast approaches deliver not only the best patient care but also improve health system efficiency. Use of Xception with transfer learning represents a quantitative leap in diagnosis of breast cancer with the use of mammogram images. The objectives of this study were to chart suitable integration in terms of partnership grade, in precision, efficiency, and commitment to data privacy, therefore making it an invaluable tool in the betterment of patient care and optimal efficiency of health care systems in breast cancer diagnosis.

The possibility to have fine-tuning in the layers, including the last layer, makes it very sensitive for very minor indicators in the mammogram images for finishing the last layer of DenseNet meant for this job. This study targeted the rise of precision in diagnosis, which is perhaps the most central key goal in breast cancer diagnosis. The problems related to the detection of breast cancer can be solved by facilitating the triage of the cases. It also speeds actions to quantify cases that require immediate actions. With this capability, the vanishing gradient problem, which is a critical issue, becomes addressable. The DenseNet will provide a leaping improvement to mammogram-based breast cancer diagnosis with this integration. The results confirmed the actuality of DenseNet with regard to precision in breast cancer diagnosis.

Explainable artificial intelligence (XAI) has emerged as a critical field in machine learning, addressing the need for transparency and interpretability in AI-driven decision-making processes [56] [57] . XAI enables users and stakeholders to understand, trust, and effectively manage AI solutions by providing insights into the model’s reasoning. One notable technique within this domain is Gradient-weighted Class Activation Mapping (Grad-CAM), which enhances the interpre- tability of DL [58] . Grad-CAM works by producing visual explanations for decisions made by the model. It highlights the specific areas in an input image that are important for predictions, allowing users to see which features the model focuses on, thus demystifying the decision-making process and increasing trust in the technology, especially in critical applications.

The verification phase in our study leveraged the advanced capabilities of Grad-CAM for a nuanced assessment of our deep learning models applied to breast cancer detection. This method offers a compelling blend of qualitative insights and quantitative analysis, elucidating the model’s decision-making process through sophisticated visualization techniques.

$L_{\text{Grad-CAM}}^c=\text{ReLU}\left(\underset{k}{{\displaystyle \sum }}{\alpha }_k^c{A}^k\right)$ (4)

Here, $L_{\text{Grad-CAM}}^c$ represents the class-discriminative localization map for a target class $c$, with ${\alpha }_k^c$ denoting the neuron importance weights for the $k^{th}$ feature map activation $A^k$. The application of the ReLU function ensures a focus on the positively influencing features of class $c$.

Incorporating Grad-CAM into our methodology brings forth several pivotal contributions:

1) Enhanced Model Interpretability: Grad-CAM significantly augments the interpretability of our models, offering intuitive visual explanations for their predictions. Establishing trust and understanding requires this level of openness, especially in a therapeutic setting where explainability is just as vital as accuracy.

2) Targeted Model Optimization: By analysing Grad-CAM heatmaps, we may improve our models’ accuracy and reliability in diagnostic prediction while simultaneously reducing bias.

3) Contribution to AI Explainability: By going beyond traditional performance verification, Grad-CAM helps us understand how deep learning models work in medical imaging. This has a big impact in the new field of explainable AI in healthcare.

4) Alignment with Clinical Insights: Our AI-driven diagnostic tools are more reliable and applicable in clinical settings when Grad-CAM marks the important diagnostic areas in the pictures. This guarantees that the model’s decisions are in line with clinical interpretations.

By strategically using Grad-CAM, our work establishes a new standard for creating interpretable, clinically-aligned AI solutions for medical diagnostics and confirms the effectiveness of our models.

The effectiveness and accuracy of ML algorithms can only be determined by measuring their performance. For this goal, our technique makes use of a full suite of measurements and tools, such as a confusion matrix, ACC, PREC, REC, F1 score, and visual representations.

$\text{ACC}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+\text{FN}}$ (5)

$\text{PREC}=\frac{\text{TP}}{\text{TP}+\text{FP}}$ (6)

$\text{REC}=\frac{\text{TP}}{\text{TP}+\text{FN}}$ (7)

$\text{F1-Score}=2\cdot \frac{\text{PREC}\times \text{REC}}{\text{PREC}+\text{REC}}$ (8)

Our approach innovatively incorporates advanced visualization techniques, dynamically displaying the relationship between accuracy and model loss over the training and validation phases. This visualization aids in assessing the learning process more effectively. Through such a detailed evaluation strategy, our model not only proves its efficiency in breast cancer (BC) detection but also facilitates ongoing enhancement and fine-tuning. This thorough analysis positions our deep learning-based model as a leading example of machine learning applications in medical diagnostics, showcasing the significant potential of advanced analytics in healthcare.

Our experiment involved a series of computational steps in a Python environment to analyze medical imaging data for breast cancer detection. The setup consisted of the following key components:

Library and Dataset Preparation

Data Preprocessing and Organization

Initial Data Exploration

Following these procedures set the stage for training and analysing deep learning models using the Grad-CAM method. The success and dependability of our experimental method for identifying breast cancer using sophisticated image processing depends on this setting.

At this pivotal point in our study, we submitted the challenge of breast cancer identification in mammography pictures to four state-of-the-art deep learning models: MobileNet, Xception, DenseNet, and each had its own unique architectural design.

MobileNet sets itself apart by making use of depthwise-separable convolutional layers, which improve model performance while maximising processing economy. Figure 2 displays the MobileNet method’s training dynamics across 100 epochs, showcasing its accuracy and loss. The remarkable capacity of MobileNet to detect complex patterns in medical imaging data was shown in the 100th training epoch, when the network achieved an accuracy of 99.51% with a loss of just 0.0129.

Figure 3 illustrates the confusion matrix for the MobileNet model, displaying the classification results between benign (label 0) and malignant (label 1) breast cancer cases. The matrix shows that the model correctly predicted 361 cases as benign and 321 cases as malignant. However, there were 29 benign cases misclassified as malignant and 10 malignant cases misclassified as benign. This matrix effectively captures the model’s accuracy in distinguishing between the two classes, demonstrating a high degree of sensitivity and specificity in its predictions.

Xception, with its three dense layers, showed robustness in feature extraction, as reflected in its high accuracy of 99.31% and a loss of 0.0200. Designed for complex image recognition tasks, its architecture is particularly effective for medical image analysis. Figure 4 showcases the training dynamics over 100 epochs, illustrating a rapid improvement in accuracy concurrent with a decrease in loss. This visualization confirms the model’s robust learning capabilities and underscores its suitability for high-stakes environments like medical diagnostics.

Figure 5 presents the confusion matrix for the Xception model, used to classify breast cancer cases as either benign (label 0) or malignant (label 1). The matrix indicates that the model successfully identified 335 cases as benign and 303 cases as malignant. On the other hand, there were 55 instances where benign cases were incorrectly labeled as malignant, and 28 malignant cases were misclassified as benign. This visual representation highlights the model’s diagnostic accuracy and areas where further tuning could enhance its ability to differentiate between the two categories more precisely, ensuring a more reliable clinical assessment tool.

DenseNet leverages a unique configuration of dense layers interconnected by dropout layers to achieve an accuracy of 96.56% and a loss of 0.0857 by the 100th training epoch, as shown in Figure 6 . The model exhibits rapid enhancements early on, marked by a sharp increase in accuracy and a swift decrease in loss. Following these initial gains, the loss levels off and remains low, while the accuracy continues to climb, demonstrating DenseNet’s ability to effectively fine-tune its performance over time. This graph underscores the model’s robust learning curve and its adeptness at maintaining a consistently low error rate, affirming DenseNet’s efficacy and suitability for complex tasks like medical image analysis.

Figure 7 displays the confusion matrix for the DenseNet model used in classifying breast cancer into benign (label 0) and malignant (label 1) categories. The matrix reveals that the model correctly identified 332 benign cases and 304 malignant cases. However, there were 58 false positives, where benign cases were incorrectly classified as malignant, and 27 false negatives, where malignant cases were misclassified as benign. This visualization provides a clear depiction of the model’s performance, highlighting its strengths in accurate classification as well as areas where improvements could be made to reduce misclassification.

Interpretation and Implications: The comparative performance evaluation of deep learning models, as summarized in Table 2 , reveals distinct differences in the outcomes achieved by MobileNet, Xception, and DenseNet under the same training conditions. MobileNet outperforms the other models with an impressive accuracy of 94.17% and the lowest loss in the last training epoch at 0.0129. Its architecture, featuring depthwise-separable convolutional layers combined with ReLU and Softmax activation functions, and optimized using Adam, enables high efficiency and effectiveness, as reflected in its F1-scores of 0.95 for benign and 0.94 for malignant classifications.

Xception, which utilizes three dense layers and the same activation functions and optimizer, shows a lower performance with an accuracy of 85.17% and a loss of 0.0200. Its F1-scores for both benign and malignant classifications are equal at 0.86, indicating a balanced performance but with room for improvement in accuracy and loss reduction.

DenseNet, incorporating five dense layers interspersed with three dropout layers, achieves an accuracy of 87% and a higher loss of 0.0523. Similar to Xception, its F1-scores are slightly better at 0.87 for benign and 0.86 for malignant, which suggests good model reliability but highlights potential areas for optimization in terms of loss reduction and overall accuracy.

The interesting findings of a comparative investigation regarding the model’s performance in classifying two categories of breast cancer are benign and malignant were presented. We did an evaluation of performance in terms of accuracy, which becomes a very important indicator for using medical imaging applications. Of the models for performance evaluation, MobileNet was at the top with an average correct classification of 94.17%. DenseNet and Xception showed a difference in accuracies of 86.68% and 85.71%, respectively. This shows, very well, that MobileNet is the best effective model in our study for the detection of breast cancer from mammogram images. Further justification towards the performance superiority of MobileNet was made through a gradient-weighted class activation mapping (Grad-CAM) technique. It is an advanced visualization tool that allows us a window into the areas of focus of the model while predicting and shows the features of the image that are mostly important in taking the decision about the presence of breast cancer.

Insights from Grad-CAM Analysis:

In Figure 8 , the heatmap overlaid on the original mammogram reveals significant areas of interest that the model focuses on, predominantly along the denser regions of the breast tissue, which are critical for detecting malignancies. Similarly, Figure 9 presents another heatmap highlighting areas where the model detects potential anomalies. These regions, marked by warmer colors (reds and yellows), indicate the zones most influential in the model’s prediction process, thus providing valuable insights into the neural network’s focus areas. Such visualizations not only aid in verifying the model’s accuracy but also contribute to clinical decision-making by pinpointing specific areas within the breast that may require closer examination. These heatmaps validate the model’s diagnostic reasoning and offer a transparent tool for clinicians to assess the AI’s analytical process.

Implications of Comparative Analysis: The study compares deep learning models in medical image analysis, highlighting the importance of model architecture and feature detection strategies. The superior performance of MobileNet in breast cancer diagnostics was evident through quantitative metrics and qualitative Grad-CAM visualizations. This study highlights the need for continued advancements in this field, with a focus on more precise and reliable AI-based diagnostic tools. The analysis also highlights the value of visualization techniques, such as Grad-CAM, in interpreting and enhancing deep learning models for medical imaging, as shown in Figure 8 .

A detailed comparison of our proposed methodology with conventional approaches is presented in Table 3 . Table 3 compares the performances of various studies employing machine learning and deep learning methodologies for image classification tasks. In our study, we used four different models: MobileNet, Xception, DenseNet, and MobileNet-LSTM, and achieved impressive results with high accuracy. Our results demonstrated that MobileNet (94.17%) outperformed the accuracies reported in other studies. For example, in [30] , accuracies ranging from 93.25% to 93.97% were achieved using conventional ML and DL techniques, In contrast [31] ’s ML-based models achived an accuracy of 91%. [32] ’s deep learning algorithms achieved an accuracy of 94.17%, slightly higher than our MobileNet model but lower than our Xception model. Moreover, our study’s performance surpassed that of [33] , which achieved a classification accuracy of 93.29%. Similarly, our models demonstrated significantly higher accuracy than the DCNN models in [34] , which reported classification accuracy ranging from 79.4% to 83.4%. [36] outlined ten open research challenges in deep learning and imaging techniques but did not report specific accuracies values.