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PReMI’23

Dr. Nita Parekh and her student Suba S presented a paper on attention-CNN Model for COVID-19 Diagnosis Using Chest CT Images  at the 10th International Conference on Pattern Recognition and Machine Intelligence (PReMI’23) held at Indian Statistical Institute, Kolkata from 12 to 15 December. 

Here is the summary of the research work as explained by the authors:

Deep learning assisted disease diagnosis using chest radiology images to assess severity of various respiratory conditions has garnered a lot of attention after the recent COVID-19 pandemic. Understanding characteristic features associated with the disease in radiology images along with variations observed from patient-to-patient and with the progression of disease is important while building such models. In this work we carried out comparative analysis of various deep architectures with the proposed attention-based Convolutional Neural Network (CNN) model with customized bottleneck residual module (Attn-CNN) in classifying chest CT images into three categories, COVID-19, Normal, and Pneumonia. We show that the attention model with fewer parameters achieved better classification performance compared to state-of-the-art deep architectures such as EfficientNet-B7, Inceptionv3, ResNet-50 and VGG-16, and customised models proposed in similar studies such as COVIDNet-CT, CTnet-10, COVID-19Net, etc.

December 2023