Image based Lung Disease Detection: Comparing Swin Transformers and ConvNets
- Resource Type
- Conference
- Authors
- Mehar, Aaditya; Shah, Mirat; Sawant, Rupali
- Source
- 2023 3rd Asian Conference on Innovation in Technology (ASIANCON) Innovation in Technology (ASIANCON), 2023 3rd Asian Conference on. :1-4 Aug, 2023
- Subject
- Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Fields, Waves and Electromagnetics
General Topics for Engineers
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Technological innovation
Pathology
Head
Pulmonary diseases
Neural networks
Transformers
Multitasking
lung disease detection
transformers
convolutional neural network
- Language
Each year, chest diseases prematurely claim 4 million lives. They are very difficult to detect even for experienced radiologists. Thus, there is a need to develop artificial intelligence-based detection systems to aid in the diagnosis of chest diseases. The paper proposes a Swin Transformer based approach to classify images into one or more out of 14 chest diseases. The model is trained using a large number chest X-ray images from the Chest X-ray14 dataset. The proposed architecture uses multiple projection heads for improved results. The proposed model is compared to existing solutions based on Convolutional Neural Networks available for the problem and produces an AUC of 0.801 with state-of-the-art performances in four pathologies.