Skin cancer is among the most prevalent malignancies globally and continues to pose a growing public health burden. Global cancer statistics indicate that in 2022 alone, approximately 1.23 million cases of non-melanoma skin cancer (NMSC) and 331,722 newly diagnosed cases of cutaneous melanoma were diagnosed worldwide, with NMSC being the 5th most common cancer and melanoma being the 17th most common cancer worldwide [1][2]. The rising incidence of skin cancer has been attributed primarily to increased ultraviolet radiation exposure, aging populations, cumulative environmental exposure, and lifestyle-related behavioral changes, particularly in high-income regions such as Oceania, North America, and Europe [1][3]. There are two major types of skin cancer, namely melanoma and non-melanoma, with basal cell carcinoma and squamous cell carcinoma. Despite melanoma representing a comparatively low percentage of overall skin cancer incidence, it causes most skin cancer-related deaths [4]. Global estimates indicate that melanoma caused approximately 58,667 deaths worldwide in 2022, compared with 69,416 deaths from NMSC, despite melanoma occurring at nearly one quarter the incidence of NMSC [5]. This overrepresentation of mortality is an expression of the aggressive biological nature and high metastatic potential of melanoma [6].

The most important factor in melanoma prognosis is early diagnosis. Surveillance, Epidemiology, and End Results (SEER) program and American Cancer Society population-based survival data indicate that, when melanoma is diagnosed at an early stage, the 5-year relative survival rate is more than 99 percent [6]. Conversely, survival decreases significantly with disease progression to about 76 percent with regional disease and 35 percent with distant metastatic melanoma [7].

Skin cancer is primarily diagnosed by visual examination of the lesion followed by dermoscopy. However, histopathology of skin lesions remains the gold standard of diagnosis. Dermoscopy, a non-invasive in vivo imaging technique, enhances the visualization of subsurface skin structures that are not detectable with the naked eye. The use of dermoscopy alongside routine clinical examination has been associated with a marked improvement in diagnostic accuracy [8].

Evidence from systematic reviews and meta-analyses indicates that dermoscopy-assisted assessment improves melanoma detection. However, its effectiveness is closely linked to the clinician’s level of expertise. A large meta-analysis reported that performance declined considerably among less experienced practitioners and those in primary care settings [9]. Across studies, overall diagnostic accuracy for dermoscopy in melanoma detection commonly falls within the ~75% - 85% range, reflecting considerable interobserver variability [10]. This inconsistency may cause false positive diagnoses and false negative assessments, which in turn may result in unnecessary biopsies, despite the fact that it may cause a delay in potentially lifesaving treatment [11].

Recent advances in dermatologic imaging have introduced additional non-invasive diagnostic modalities, including reflectance confocal microscopy (RCM), optical coherence tomography (OCT), and multiphoton microscopy [12]. These technologies offer high-resolution structural and, in certain instances, quasi-histological imaging of skin lesions and have been shown to have better diagnostic capability for specific skin cancers in special environments [13]. Nevertheless, their wider clinical implementation is limited by the high costs of equipment, limited image penetration, time-consuming image capture, and special training and experience, limiting their utilization in general dermatology practice and low-resource environments [14]. Therefore, in spite of technological advances, there is still a significant gap in unmet demand for scalable, accessible, and accurate diagnostic solutions capable of decreasing diagnostic variability and supporting the early diagnosis of skin cancer [15].

Artificial intelligence and in particular deep learning have emerged as disruptive technologies in medical imaging. Convolutional Neural Networks (CNNs) have been identified as more effective in eliciting hierarchical features in complex image data, allowing automated classification of data with high accuracy [16]. CNN models trained on dermoscopic image data sets, including the International Skin Imaging Collaboration, have matched expert dermatologists in performance, and in some cases, surpassed their accuracy [16].

These models reduce intra-observer reliability and thereby result in reduced subjectivity. Despite these developments, challenges still exist. Deep learning models rely on large, closely annotated datasets. Such datasets are not always readily available in medical studies [17]. Imaging modalities and lighting variations, coupled with resolution and acquisition device variations, may affect the performance of models and limit their generalizability across clinical locations [18]. Moreover, the fact that CNN models are not decipherable poses a problem for clinical trust and usage [19]. These inadequacies show the significance of strong, generalizable, and explainable models which can be applied in clinical practice. The current diagnostic techniques for skin cancer still fall short due to subjectivity, inter-observer variability, and lack of access to quality dermatological services [20]. Although dermoscopy boosts diagnostic ability, this technique is also experience- and training-sensitive, thereby providing imbalanced outcomes across different places [21]. The lack of dermatologists in resource-constrained areas also contributes to delays in diagnosing and treating [22].

The existing deep learning models are promising, but with limitations [23]. Most of the models lack accuracy and reliability when exposed to various datasets. This could be attributed to differences in skin type and lesion morphology. The imaging conditions are crucial in determining the accuracy of detection [24][25]. Moreover, the reliance on sizable annotated datasets presents a significant impediment because data gathering and labeling are time-consuming and involve the participation of experts [26]. Another significant disadvantage of CNN models that hinders interpretability and reduces confidence in automated decisions among clinicians is the black-box nature of these models [27]. Hence, it is apparent that a robust and universally applicable deep learning model capable of effectively categorizing dermoscopic images as benign and malignant and overcoming the issues associated with data variability, model generalization, as well as clinical applicability, is necessary [28].

The main aim of this research was to design and test a deep learning-based diagnostic algorithm to detect skin cancer automatically with the use of dermoscopic images. Specifically, this study aimed to design and implement a CNN-based architecture for the binary classification of skin lesions into benign and malignant categories. This study aimed to improve the generalization of models by conducting systematic preprocessing and data augmentation to boost the performance of models. Moreover, this study assessed the diagnostic performance of the proposed model based on conventional measures of performance, such as accuracy, sensitivity, specificity, and AUC-ROC. Another objective was to compare the model’s performance with traditional dermatological diagnostic methods. Lastly, the study explored model interpretability through visualization techniques such as Grad-CAM to support clinical applicability and decision-making.

This study has shown that a CNN-based model can obtain good diagnostic performance during automated skin cancer detection and that its accuracy, sensitivity, specificity, and AUC-ROC are 90.3, 92.1, 88.5, and 0.95, respectively (Table 2). These findings are in line with earlier research that has reported high performance of deep learning models in dermoscopic image analysis [30]. We also outperformed the average diagnostic ability of dermatologists (75 - 84) in our model (Table 3), which is evidence that AI systems can positively influence diagnostic consistency and minimize inter-observer variation [31].

As demonstrated in the confusion matrix (Table 1; Figure 3), the performance of classification is high, with relatively low false negatives, and this factor is crucial for clinical safety. Nonetheless, the occurrence of false positives, especially in non-typal benign lesions, is also indicative of a typical limitation also reported in other existing studies [24].

The comparative analysis also showed that the proposed model performed better than conventional architectures like VGG-16 and VGG-19 (Table 4), which is in line with the results that task-specific CNN design can offer better performance as compared to generic pre-trained models [32]. Although, in certain cases, more complex or ensemble models may be found to provide higher accuracy, they are frequently developed in controlled settings and might not be generalizable [33]. Lastly, interpretability using Grad-CAM (Figure 4) indicated that the model targeted clinically pertinent aspects like asymmetry and pigmentation, which were consistent with existing dermatological criteria and results of previous AI research [19].

The results of this research are also indicative of the trends in the use of AI in medical imaging (Figure 5). CNN-based methods have proven to be useful in dermatology and other fields like breast cancer detection and neuroimaging [30]. This cross-domain consistency improves the strength of CNN architecture in complex image classification problems (Figure 5). However, in all applications, there remain problems of data variability, interpretability, and generalization [26].

Diagnostic tools based on AI can be very helpful clinically. They have the ability to aid in early-stage diagnosis, enhance the precision of diagnosis, and decrease the strain on the clinical staff, especially in resource-constrained environments where access to dermatological expertise is scarce [5]. However, AI systems must be viewed as a supplement, and not a substitute for clinicians because complex and ambiguous cases must be diagnosed under the supervision of a human. Although the results are promising, there are certain limitations which should be considered. It can be limited to a single dataset; this can be a constraint in extrapolation to a wide variety of other populations, skin types, and imaging conditions. This has been identified as a significant obstacle to clinical translation studies [16]. Moreover, the model has demonstrated average performance, which is high, but the fact that the model does possess false negatives indicates that subtle or early lesions are difficult to detect. The second weakness is related to the inherent opaqueness of CNN models, since explanatory algorithms such as Grad-CAM are partial explanations. In addition, this study was not prospectively clinically validated, which should be done to ensure that it can be applied in the real world. Finally, performance when conditions are not experimental can be influenced by imprecision in imaging conditions and presentation of data [34].

Clinically, incorporation of AI-based diagnostic tools has its benefits. They can contribute to early diagnosis, improve diagnostic accuracy, and reduce the burden on clinicians, particularly in resource-limited settings where access to dermatological expertise is limited [5]. Nevertheless, AI systems are not supposed to be regarded as substitutes for clinicians but as supportive tools in cases of complex and ambiguous situations, since human supervision is still a necessity.

This study has limitations. The results are based on a single dataset, precluding generalizability. The applicability and suitability of this model to a variety of different populations, skin types, and imaging conditions need to be studied. Studies have acknowledged this as a significant obstacle to clinical translation [26]. Moreover, the model has demonstrated high average performance, but the fact that the model does include false negatives shows that subtle or early-stage lesions are difficult to identify. The other weakness is associated with the intrinsic opaqueness of CNN models because interpretable algorithms like Grad-CAM only provide partial explanations. Furthermore, this model was not prospectively clinically validated, which must be established to ensure that it is applicable in the real world. Lastly, the imprecision of imaging conditions and dataset presentation can affect performance under non-experimental conditions [34].

Future studies should focus on improving model accuracy and robustness through the use of ensemble approaches and transfer learning applied to extensive and heterogeneous medical datasets, particularly to manage ambiguous cases and populations with limited representation.

The proposed study shows that a CNN-based model can be used to correctly classify dermoscopic images as benign or malignant lesions, with the model having a high diagnostic performance (accuracy 90.3%, sensitivity 92.1%, specificity 88.5%, AUC-ROC 0.95). The results support the assumption that preprocessing and data augmentation can enhance the generalization and stability of the models significantly, and the suggested architecture is better than the standard ones, including VGG-16 and VGG-19. The model proved to be very clinically relevant with a low false negative rate and was capable of highlighting diagnostically significant features with Grad-CAM visualization. These findings suggest that CNN-based systems can be used as effective decision-support techniques, which enhance diagnostic consistency and aid in the early diagnosis of skin cancer. Findings of this study demonstrate that task-specific deep learning models can be a feasible and scalable solution to skin cancer detection and could be incorporated into clinical practice, especially in resource-constrained environments. Future studies should focus on ensemble and transfer learning, improved explainable AI, real-world clinical validation, and ethical and regulatory compliance to strengthen safe clinical adoption.