Efficient Skin Lesion Identification using Deep Learning with VGG19

This research explores how the VGG19 deep learning architecture allows for skin lesion diagnosis through identification of dermoscopic images. The clinical challenges of skin cancer are substantial especially for melanoma because this cancer forms metastases fast. Early detection plays a vital role in boosting patient survival possibilities. The current diagnostic process that depends on dermatological professional encounters delays and geographical limitations during assessment. The proposed method applies VGG19 transfer learning together with pre-trained weights and customized additional classification layers which target the particular dataset. The system includes data augmentation methods together with class imbalance solutions to boost generalization and accuracy levels. A wide range of skin lesion categories formed the basis for training and testing the developed model. The proposed system reached 92.33 % training accuracy alongside 88.00 % validation accuracy and delivered precision of 88.36 %. Analysis of confusion matrices showed effective class recognition throughout all lesion types, but minor mistakes occurred between lesions with similar characteristics. The research demonstrates deep learning models have potential to establish automatic dermatological diagnosis systems which improve healthcare service accessibility by combining automated processes with efficiency.