Computer Vision with Machine Learning Enabled Skin Lesion Classification Model

Recently, computer vision (CV) based disease diagnosis models have been utilized in various areas of healthcare. At the same time, deep learning (DL) and machine (ML) play a vital role healthcare sector for effectual recognition diseases using medical imaging tools. This study develops novel with optimal enabled skin lesion detection classification (CVOML-SLDC) model. The goal CVOML-SLDC model is to determine appropriate class labels test dermoscopic images. Primarily, derives gaussian filtering (GF) approach pre-process input images graph cut segmentation applied. Besides, firefly algorithm (FFA) EfficientNet feature extraction module applied derivation vectors. Moreover, naïve bayes (NB) classifier application FFA helps effectually adjust hyperparameter values experimental analysis performed benchmark dataset. detailed comparative reported improved outcomes over recent approaches maximum accuracy 94.83%.