Year: 2024
Type: article
Abstract: This study investigates the classification of skin cancer lesions utilizing Convolutional Neural Networks (CNNs) on the HAM10000 dataset. The collection includes photos of seven unique categories of skin lesions: melanocytic nevi, melanoma, benign keratosis-like lesions, basal cell carcinoma, actinic keratoses, vascular lesions, and dermatofibroma. Our main goals were to examine diagnostics efficacy over several lesion types and assess CNN performance in precisely categorizing dermatoscopic pictures. Label encoding and data balancing were used to ensure class representation. For regularization, we built sequential CNN models with convolutional, pooling, and dense layers and dropout and batch normalization. Transfer learning and Adam optimization were used in training. Our classification accuracy averages above 73.8%, demonstrating solid performance across lesion types. We tested the models' generalization ability on new data and found consistent performance. Confusion matrix analysis showed accurate categorization with low misclassification. This research improves early identification and intervention, improving patient outcomes and healthcare efficiency.
Source: Journal of Carcinogenesis
Authors: Manoj Manoj, Jaskirat Kaur, Manpreet Kaur, Sanju Sanju, Rajinder Kumar, Deepender Deepender
Institutions: Sri Guru Granth Sahib World University, Guru Kashi University
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $1,500
OpenAlex ID: W4414277159