Proactive Cancer Screening Through Convolution Neural Networks
DOI:
https://doi.org/10.63682/jns.v14i32S.7308Keywords:
Computer vision in healthcare, Image classification, CNN, Dermatology, HAM10000Abstract
Melanoma represent one of the most perilous types of skin cancer due to their rapid progression and the challenges associated with diagnosis. This research used the HAM10000 dataset to demonstrate Convolutional Neural Networks (CNNs), the most sophisticated deep learning model for classifying skin cancer lesions. During this investigation, we gathered 10,015 dermatoscopic images and classified them into seven separate kinds of skin lesions. The model performs feature extraction and classification hierarchically using fully connected, pooling, and convolutional layers. This endeavor has resulted in an impressive 98.57% training accuracy and 93.34% validation accuracy, representing a substantial improvement over the previously used approach. Essential performance metrics, such as accuracy, recall, and F1-score, demonstrate the model's efficacy in detecting different types of skin cancer. We obtained high accuracy, an F1 score, and sub-optimal recall. The evidence indicates that CNN-based approaches may facilitate early diagnosis, improve treatment results, and reduce dermatologists' workloads. This study's results contribute to the advancement of skin cancer research
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