Using Deep Learning and Medical Data Classification for Predicting Bacterial Skin Infection
Bacterial skin infections are one of the most common types of infections in medical sector. Normally, it is being diagnosed visually starting with an initial clinical screening and then possibly followed by dermoscopic analysis, Antibiotics both oral and topical histopathological examination. In this paper I would focus on deep learning approaches like CNN and Medical Data Classification for Predicting Bacterial Skin Infection. The Application of Deep learning is continuously being used to determine the accuracy of detecting different medical problems more effectively. A lot of new techniques have been discovered to fast forward the procedure with having highest percentage of accuracy. In this paper work, we have proposed a model to detect bacterial infections more effectively using image processing with convolutional neural network, a part of deep learning concept under machine learning. The dataset contains almost 3000+images of the patients having skin diseases classified into two classes, malignant and benign. We have introduced CNN along with its seven different architectures to find the accuracy of the images of skin bacterial infections and performed a comparative analysis to find out the best architecture that suits this type of problem. For the training, testing and validation of our dataset a well known MATLAB R2019a software was used for this purpose. We performed the best results Using Deep Learning and Medical Data Classification with having almost 91.303% accuracy to determine skin infections.
2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)