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Lung Cancer Classification from Chest CT Scans Using EfficientNet

Year: 2026

Type: article

Abstract: Lung cancer is the leading cause of cancer-related deaths, and early detection is critical for improving survival.This study presents an explainable deep learning approach for classifying chest CT scans into normal, benign, and malignant categories.Six pretrained convolutional neural networks were evaluated on the public IQ-OTH/NCCD dataset using class-weighted training and light data augmentation.EfficientNet-B0 achieved the best performance, with 99.39% accuracy, a 0.988 macro F1score, and 0.996 AUC, while correctly identifying all malignant cases.

Source: International Journal of Computer Applications

DOI: 10.5120/ijcacc863343a259

Authors: Rashedur Rahman, Shahjadi Sultana, Mithun Das, Akbor Aziz Susom, S.A. Sabbirul Mohosin Naim, Numair Bin Sharif

Institutions: San Francisco Bay University, Lamar University, International American University, The University of Texas at San Antonio

Language: English

Topic: AI in cancer detection

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: diamond

OpenAlex ID: W7167417949

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