DIAGNOSIS OF SKIN DISEASES USING MODIFIED CONVOLUTIONAL NEURAL NETWORKS
PRIYADARSHINI M, POOJA V, KEERTHANA S, Dr. A. SUMAIYA BEGUM
Convolution, feature extraction, Medical Image Processing, Machine Learning classification, Dermatology, Image processing, Deep learning, Computational Intelligence, Automated Diseases Diagnosis
Dermatology is one of the most unpredictable and difficult terrains to diagnose due to its complexity. In the field of dermatology, many a times extensive tests are to be carried out so as to decide upon the skin condition the patient may be facing. The Time may vary from practitioner to practitioner. This is also based on the experience of that person too. So, there is a need of a system which can diagnose the skin diseases without any of the constraints. We propose an automated image based system for recognition of skin diseases using machine learning classification. This system will utilize computational technique to analyse, process and relegate the image data predicated on various features of the images. Skin images are filtered to remove unwanted noise and also process it for enhancement of the images. Feature extraction using complex techniques such as Convolutional Neural Network(CNN),classify the image based on the algorithm of logical regression and obtain the diagnosis report as an output. This system will give more accuracy and will generate results faster than the traditional methods, making this application an efficient an dependable system for dermatological diseases detection. Futhermore, this can also be used as a reliable real time teaching tool for medical students in the dermatology stream.