Skin Disease Classification and Detection Using Deep Learning

Abstract

The integumentary system is a remarkable component of the human anatomy, frequently susceptible to an array of recognized and unrecognized maladies. There exists a multitude of prevalent disorders, some of which rank among the most ubiquitous globally. The identification of these ailments can present challenges due to disparities in cutaneous consistency, the presence of follicles, and variations in pigmentation. Furthermore, in remote regions with limited access to medical facilities, individuals may disregard initial symptoms, thereby exacerbating their conditions progressively. The diagnosis of dermatological conditions may also prove to be considerably time-intensive. The implementation of machine learning techniques is imperative for enhancing the precision of diagnostic procedures for diverse skin afflictions. Deep learning methodologies, commonly utilized in medical domains, scrutinize image feature parameters to render diagnostic determinations. This progression encompasses three primary phases: feature delineation, model training, and evaluation, leveraging machine learning algorithms to assimilate insights from an assortment of dermatological images. The primary objective is to amplify the accuracy of detecting skin diseases through this computational framework. The proposed system incorporates the Convolution Neural Networks (CNN) for efficient classification and detection of the skin diseases. CNNs have demonstrated unparalleled efficacy in tasks pertaining to visual perception. The endeavor at hand involves the formulation of a CNN classifier utilizing Python, with Keras and Tensor Flow as the underlying computational tools. The exploration of diverse network configurations will entail the assessment of various layer types, including Convolutional, InceptionV3, Dense, and Pooling layers. This envisioned system attains a peak accuracy level of 90% within this specified model.