Bat Optimized CNN For Skin Cancer Detection Using Deep Learning Approach

Among the most harmful forms of malignancy that people get easily these days is skin cancer. Skin cancer comes in a variety of forms, including basal, melanoma, carcinoma, and squamous cell, among which melanoma is unexpected. Therefore, earlier skin cancer diagnosis is crucial for successful treatment. In order to diagnose skin cancer, this research proposes a new technique dubbed the bat optimisation algorithm. To reduce the noise and artefacts from the input image, pre-processing is first applied. The pre-processed image is then passed on to the feature extraction stage, where the features are obtained using convolutional neural network features. A bat optimisation is then employed to classify data based on the retrieved features. The precision, efficacy, and specificity of skin cancer diagnosis using the proposed approach are assessed.