TITLE:
Augmented Lung Cancer Prediction: Leveraging Convolutional Neural Networks and Grey Wolf Optimization Algorithm
AUTHORS:
Teresa Kwamboka Abuya, Wangari Catherine Waithera, Cheruiyot Wilson Kipruto
KEYWORDS:
Machine Learning, Lung Cancer, False Negative Rate, Grey Wolf Optimization, Bin Smoothing, Convolutional Neural Networks, Optimization Algorithms
JOURNAL NAME:
Open Access Library Journal,
Vol.11 No.4,
April
12,
2024
ABSTRACT: With the rapid increase in population, the rate of diseases like cancer
is also increasing. Lung cancer is a leading cause of cancer-related deaths
with a minimum survival rate; there is a need to find better, faster, and more
accurate methods for early diagnosis of this disease. Although previous
research in lung cancer has presented numerous prediction schemes, the feature
selection utilized in the schemes and learning process has failed to enhance
the accurate performance of lung cancer diagnosis, including incorrect
classification and low prediction levels, which lead to misdiagnosis. Prediction
of lung cancer cells from lung images in early stages is a question mark for
researchers. This study presents a discerning way of predicting lung cancer
with the Grey Wolf Optimization Algorithm (GWOA) and Convolutional Neural
Networks (CNN). The 14,740 CT scan images are used for classification. The
Kaggle dataset, data preprocessing, hyper-parameter feature selection using
GWOA, classification using CNN, RF, and DT, cross-validation, and classifier
evaluation are the five phases of the proposed lung cancer prediction
architecture. The noise present in the data was eliminated by applying a bin
smoothing normalization process. In terms of lung cancer prediction, we show
that the highest score is achieved when applying CNN with GWOA, which produced
the best results with an average performance of 96% accuracy, F1-score,
precision, and recall, respectively compared to RF and DT with GWOA. Similarly,
the CNN-GWOA produced the lowest false negative rate (FNR) of 0.023676. The low
FNR means that it was possible to diagnose lung cancer with very minimal
incorrect classification errors. This translates to successful prediction of
lung cancer disease correctly.