Efficient NetB3 for Enhanced Lung Cancer Detection: Histopathological Image Study with Augmentation

Document Type : Research Paper


1 Department of CSE Annamacharya Institute of Technology and Sciences, Tirupati, India.

2 Department of Information Science and Engineering, BMS Institute of Technology and Management, Bengaluru, Karnataka.

3 Department of Machine Learning (AI-ML) BMS College of Engineering, Bangalore, India.

4 Department of Database Systems, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore - 632014, Tamilnadu, India.

5 Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India.

6 Department of Electronics and Communication Engineering, Kallam Haranadhareddy Institute of Technology (Autonomous), NH-16, Chowdavaram, Guntur, Andhra Pradesh, India.



Cancer is an abnormal cell growth that occurs uncontrollably within the human body and has the potential to spread to other organs. One of the primary causes of mortality and morbidity for people is cancer, particularly lung cancer. Lung cancer is one of the non-communicable diseases (NCDs), causing 71% of all deaths globally, and is the second most common cancer diagnosed worldwide. The effectiveness of treatment and the survival rate of cancer patients can be significantly increased by early and exact cancer detection. An important factor in specifying the type of cancer is the histopathological diagnosis. In this study, we present a Simple Convolutional Neural Network (CNN) and EfficientNetB3 architecture that is both straightforward and efficient for accurately classifying lung cancer from medical images. EfficientnetB3 emerged as the best-performing classifier, acquiring a trustworthy level of precision, recall, and F1 score, with a remarkable accuracy of 100%, and superior performance demonstrates EfficientnetB3’s better capacity for an accurate lung cancer detection system. Nonetheless, the accuracy ratings of 85% obtained by Simple CNN also demonstrated useful categorization. CNN models had significantly lower accuracy scores than the EfficientnetB3 model, but these determinations indicate how acceptable the classifiers are for lung cancer detection. The novelty of our research is that less work is done on histopathological images. However, the accuracy of the previous work is not very high. In this research, our model outperformed the previous result. The results are advantageous for developing systems that effectively detect lung cancer and provide crucial information about the classifier’s efficiency.


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