An Efficient Skin Cancer Stage Diagnostic Approach Using Customized Inception V3 Deep Learning Model

Among all stages of skin cancer, Melanoma, Basel Cell Carcinoma (BCC), and Squamous Cell Carcinoma (SCC) have a significant impact on world health. Although deep learning offers promising potential for dermatological categorization, only limited disease groups have benefited, since most studies focus on particular illnesses rather than covering comprehensive human skin problems. Computerized analysis has been used in the past to identify cancer in skin lesion images, but challenges still persist mainly due to the multiple forms, textures, and sizes of lesions that complicate skin cancer classification. This research paper presents a Convolutional Neural Network (CNN) model customized to meet our requirements by using a pre trained InceptionV3 model along with Bayesian hyperparameter tuning. Using the ISIC 2024 and HAM 10000 datasets, the main objective is to classify skin lesions and differentiate between malignant Melanoma, BCC, and SCC. By implementing this customized model, the issue caused by variations in lesion appearance is effectively addressed, leading to more accurate predictions. Using Bayesian hyperparameter tuning can increase identification while decreasing computational cost. The proposed model performed strongly on the combined datasets by achieving combined average accuracy of 95.1 %, a precision of 94.42 %, a sensitivity of 97.3 %, a specificity of 98.8 %, and an F1 score of 95.7 %. These results demonstrate that the model significantly outperformed existing techniques and provided more accurate and consistent diagnosis of pigmented skin lesions compared to current standards.