Empirical Investigations to Skin Lesion Detection Using DenseNet Convolutional Neural Network

The delivery of dermatological services could be completely transformed by the use of teledermatology. Through the use of telecommunications technologies, teledermatology is utilized to communicate medical information to experts to investigate disease. The goal of our research is to identify skin lesions by classifying the image samples of skin lesions that were obtained from various patients. In this work, input data is taken from the "HAM10000" dataset from Kaggle. In the next step, input images are resized using the computer vision library, resizing of images must be done to focus more on the lesion area, splitting of the dataset into training dataset and testing dataset is done. In the next step, 80% of the dataset is used for training and 20% is used for testing. Here we proposed DenseNet Model with five convolutional layers is trained up to 100 epochs by training dataset. The trained DenseNet model is tested on the testing dataset and the accuracy is measured and evaluated. Our experimental investigations emphasize that the detection of skin lesion of input data image.