Cancer has a tremendous present impact on human existence due to its extremely high global death rate. Malignant melanoma of the skin accounts for 20 daily deaths in the United States. Malignant melanomas (MEL), basal cell carcinomas (BCC), actinic keratoses intraepithelial carcinomas (AKIEC), nevi (melanocytic), keratinocytic lesions (BKL), dermatofibromas (DF), and vascular lesions (VL) are the seven main types of skin cancer (VASC). It might be challenging to recognize and classify different cancer kinds frombiomedical imaging, as there are many sub-cancer types that differ significantly from one another. Several researchers and doctors are currently trying to pinpoint the most effective means of spotting skin cancer in its earliest stages. Using multiple residual and sequential convolutional neural networks,we present a learning strategy for cancer classification in this research. An effort is made here to more precisely categorize MEL, BCC, and BKL cancers. F1 score, precision, recall, and accuracy are used to verify the validity of the proposed model. Results show the reliability and validity of the model.
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