A robust CNN Deep Learning and InceptionV3 model Techniques for Enhanced Skin Cancer Detection
DOI:
https://doi.org/10.29304/jqcsm.2024.16.41806Keywords:
Convolutional Neural Network (CNN), HAM10000 dataset, Deep Learning, InceptionV3, Skin CancerAbstract
Skin cancer, a dangerous form of cancer, originates from DNA damage that causes unchecked cell growth, resulting in a rapid rise in its occurrence. Earlier research has explored the application of computerised analysis for detecting malignancy in images of skin lesions. Nonetheless, challenges remain, including complications with light reflections, variations in colour, and the diverse shapes and sizes of lesions. This research explores how deep learning techniques can improve the detection of skin cancer. We developed and meticulously evaluated two models: a custom-built Convolutional Neural Network (CNN) and a tailored InceptionV3 model that was pre-trained and adapted for our needs. Our primary goal with the HAM10000 dataset was to execute binary classification, distinguishing between malignant melanoma and benign nevus. The custom Convolutional Neural Network (CNN), noted for its efficient design, achieved a precision rate of 91.80%, while the more complex adapted InceptionV3 model secured an impressive accuracy of 95.72%. The findings showcased here demonstrate the effectiveness of both tailored and pre-trained deep learning models in identifying skin cancer. This showcases their capability to enhance diagnostic accuracy and efficiency, even when encountering various resource and computing constraints. This study highlights how deep learning can significantly improve the accuracy of skin cancer diagnoses, ultimately leading to better outcomes for patients.
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Copyright (c) 2024 Bahaa Salih Mandeel, Manar Raad Abdalhassan, Diyaa Kareem Abd-Alhmza, Muhtadi Thamer Mousa Raad
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