Rhaman, Md. KhalilurShuvon, Mehedi HasanSadia, RowshanaraShormi, Shanjida HabibArafin, Umma TaniaChowdhury, Md. Rawha Mikdad2022-07-242022-07-2420222022-01ID 18101686ID 18101188ID 18101097ID 18201203ID 18101672http://hdl.handle.net/10361/17027Cataloged from PDF version of thesis.Includes bibliographical references (pages 36-37).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.Skin Diseases have been the primary focus of this study, as they are one of the most lethal diseases if not diagnosed and treated early. The research will enable the fields of Medical Science and Computer Science to collaborate in order to save lives. Although Machine Learning, Deep Learning, and Image Processing have been utilized previously to treat skin diseases, we are attempting to improve the accuracy of this work by implementing new models of Image Processing and Deep Learning. The purpose of this research is to demonstrate how to accurately diagnose Skin diseases at an early stage using the optimum model. Here we have used three distinct neural models to classify a custom dataset. We further analyzed the accuracy of the MobileNetV2, InceptionV3, and ResNetV2 to come up with an optimized model that can be configured further to a mobile application for vast use. We built the architecture on more than 1450 images representing nine distinct skin disorders in comparison with fresh skin. We carefully compared our data and classified it based on the images of our customized dataset. Finally, we determined the nine diseases with a 96.77% accuracy with the help of MobileNetV2 which is our ideal model for the goal we want to achieve.37 pagesenBrac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Image processingDeep learningMobileNetV2InceptionV3ResNetV2EpochSoftmaxSkin diseaseKNNCNNDetectionTensorflowKeras LayerDense layerMachine learningImage processing -- Digital techniques.Cognitive learning theory (Deep learning)Thesis