LRSE-LCC: A Lightweight Residual CNN with Squeeze-and-Excitation Attention for Lung Cancer Classification from CT Image

Dhaval J. Rana; Keyur Rana

doi:10.35882/jeeemi.v8i2.1574

Dhaval J. Rana Department of Computer Engineering, Gujarat Technological University, Gujarat, India https://orcid.org/0009-0008-6486-252X
Keyur Rana Department of Computer Engineering, Sarvajanik College of Engineering and Technology-SCET, Gujarat, India https://orcid.org/0000-0002-5121-8734

DOI: https://doi.org/10.35882/jeeemi.v8i2.1574

Keywords: Lung cancer classification, Computed tomography, Residual CNN, queeze-and-Excitation, Deep Learning, Medical image analysis

Abstract

Lung cancer is still a major cause of cancer deaths globally, and there is a need for accurate and early diagnostic systems. Although deep learning models have shown encouraging results in classifying lung cancer from CT scans, most are computationally complex. This paper proposes the design of a lightweight and accurate deep learning model for multi-class lung cancer classification from CT scans. A new model called Lightweight Residual CNN with Squeeze-and-Excitation Lung Cancer Classification (LRSE-LCC) is proposed. The model combines lightweight residual learning for stable gradient flow and channel attention for improved feature representation. Dual global pooling is used by combining Global Average Pooling and Global Max Pooling to enable complementary feature extraction. In addition, a balanced batch training method is used to handle class imbalance. The proposed model was tested on the IQ-OTH/NCCD lung CT image dataset, which includes normal, benign, and malignant images. Image resizing and normalization were done before training. The proposed LRSE-LCC model achieved a test accuracy of 98.19%. Sensitivity was 100.00%, indicating strong ability to detect malignant images. The model achieved a specificity of 99.04%, reducing false-positive predictions. The macro-averaged AUC was 99.90%. The AUC values for all classes exceeded 99.80%, indicating outstanding classification performance. The macro F1-score was 96.42%. The value of the Cohen’s kappa coefficient was 96.88%, which ensured that the agreement was not by chance. The overall error rate was limited to 1.81%. In conclusion, the proposed LRSE-LCC model has both high classification accuracy and efficiency. The combination of residual learning, channel attention, and dual pooling helps to greatly improve the accuracy of multi-class diagnosis. The proposed lightweight model has great potential for application in real-world computer-aided lung cancer diagnosis systems.

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