Abnormally Detection in Medical Images Using Bag of Models

Date of Award


Degree Name

M.C.S. (Master of Computer Science)


Department of Computer Science


Tam V. Nguyen


Deep Learning Neural Networks provide smart alternatives and efficient algorithms on data-driven models for data processing. The approach of learning from image data can deliver reliable findings and analysis which in turn can give us more accurate results in less time with low utilization of resources. Through neural network implementation, we can get much better results in classification which other methods fail to replicate. Neural Network-based learning has shown to be beneficial in a variety of fields, and its application has aided us to consider the implementation of the COVID-19 epidemic. The most challenging task observed is the detection of COVID-19 symptoms. The most relevant type of detecting the symptoms is from human lung X-ray images without manual intervention. For this purpose, we implemented a novel algorithm to classify the positive epidemic cases from the dataset consisting of human X-rays labeled under covid and non-covid. The dataset is trained through a series of multilayer models such as VGG16, VGG19, Inception ResnetV2, Xception, and MobileNet to extract the feature and classify the images respectively, and then finally feed into a Support Vector Machine (SVM) classifier to get the desired output. The training yielded an accuracy of 95% from this approach and can be considered for further implementation on different research topics in the near future.


Artificial Intelligence, Health Care, Health Sciences, Information Technology, Medical Imaging

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