EfficientNet-SAM: A Novel EffecientNet with Spatial Attention Mechanism for COVID-19 Detection in Pulmonary CT Scans

Ramy Farag, Parth Upadhay, Jacket Dembys, Yixiang Gao, Katherin Garces Montoya, Seyed Mohamad Ali Tousi, Gbenga Omotara, Guilherme Desouza; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 5200-5206

Manual analysis and diagnosis of COVID-19 through the examination of Computed Tomography (CT) images of the lungs can be time-consuming and result in errors especially given high volume of patients and numerous images per patient. So we address the need for automation of this task by developing a new deep learning-based pipeline. Our motivation was sparked by the CVPR Workshop on "Domain Adaptation Explainability and Fairness in AI for Medical Image Analysis" more specifically the "COVID-19 Diagnosis Competition (DEF-AI-MIA COV19D)" under the same Workshop. This challenge provides an opportunity to assess our proposed pipeline for COVID-19 detection from CT scan images. The same pipeline incorporates one of the architectures in the EfficientNet "family" but with an added Spatial Attention Mechanism: EfficientNet-SAM. Also unlike the traditional/past pipelines which relied on a preprocessing step our pipeline takes the raw selected input images without any such step except for an image-selection step to simply reduce the number of CT images required for training and/or testing. Moreover our pipeline is computationally efficient as for example it does not incorporate a decoder for segmenting the lungs. It also does not combine different models nor combine RNN with a backbone as other pipelines in the past did. Nevertheless our pipeline outperformed all approaches presented by other teams in last year's instance of the same challenge using the validation subset. It also placed 5th in this year's competition ranking less than 1.3% below the 1st place and close to 3.5% above the 6th place based on the macro-F1 score.