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Enhancing pulmonary abnormality detection with an optimized CNN architecture incorporating depth-wise separable convolution and inception module

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Abstract

Infectious lung diseases are a global health concern, and deep learning, particularly convolutional neural networks (CNNs), holds promise for diagnosing these conditions using chest x-rays (CXRs). However, existing models prioritize accuracy, often neglecting challenges in deploying on resource-limited devices. This study introduces "ConvInceptNet," a lightweight CNN leveraging depth-wise separable convolutions (DSC) and a modified Inception (M-Inception) module. Our approach addresses computational complexities, enhancing efficiency and reducing model size. We replaced the traditional convolutions in the original Inception module with DSC layers to enhance feature extraction and reduce the number of computation parameters required for the model. To further enhance detection performance and prevent overfitting in ConvInceptNet, we employed various regularization techniques such as dropout layers and data augmentation using different geometries. We validated our approach using four publicly available datasets comprising CXRs of normal cases, pneumonia, TB, COVID-19, and pneumothorax, offering a diverse range for comprehensive performance evaluation. ConvInceptNet achieved high accuracy rates for detecting various pulmonary abnormalities, including 98.90% for COVID-19, 99.22% for TB, 96.73% for pneumonia, and 99.56% for pneumothorax detection. For more accurate analysis, ConvInceptNet was benchmarked against three pre-trained models, MobileNetV2, Xception, InceptionV3 and the state of the art (SOTA) heavy models. Our statistical analysis confirmed ConvInceptNet's superior performance in accuracy, parameter efficiency, model size, FLOP counts, and inference time compared to other models. This establishes ConvInceptNet as an efficient solution for detecting pulmonary abnormalities on resource-limited mobile and edge devices.

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Data availability

The data that support the findings of this study are available from the first author upon reasonable request.

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Funding

The authors did not receive support from any organization for the submitted work.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Central South University, Changsha, China

    Sohaib Asif

  2. School of Public Health, Central South University, Changsha, China

    Qurrat-ul-Ain

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  1. Sohaib Asif
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Contributions

SA: Conceptualization, Data curation, Methodology, Software, Writing—original draft, Writing—review and editing. Qurrat-ul-Ain: Visualization, Data curation, Formal analysis, Investigation, Writing – review & editing. All authors reviewed the manuscript.

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Correspondence to Sohaib Asif.

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Asif, S., Qurrat-ul-Ain Enhancing pulmonary abnormality detection with an optimized CNN architecture incorporating depth-wise separable convolution and inception module. Evolving Systems (2024). https://doi.org/10.1007/s12530-023-09565-2

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