Publication

Abstract : Machine learning (ML) is a significant subset of Artificial Intelligence (AI) that plays a key role in medical diagnosis. The advantage of AI is they can automatically learn, extract and translate the features from data sets such as images, text or video, without introducing traditional hand-coded code or rules. This paper focuses on recognizing and classifying lung diseases by ML algorithms. It includes 400 lung disease images (i.e. CT scan images) including bronchitis, emphysema, pleural effusion, cancer, and normal. The input image is analyzed, categorized and classified using ML algorithms such as the MLP, KNN and SVM classifier. After feature extraction, the output is segmented and compares the classifier's accuracy. When a CT scan image was given to a classifier as an input, it contains irrelevant information. For the selection of the most relevant features (i.e. for extracting characteristics) here Gray Level Co-occurrence Matrix (GLCM) is used. For MLP, this classifier acquires 98% accuracy, for SVM accuracy is 70.45% and for KNN accuracy is 99.2%. These classifiers will help the doctors to prescribe the most effective treatment for a patient.