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Efficient NetB3 for Enhanced Lung Cancer Detection: Histopathological Image Study with Augmentation | ||
| Journal of Information Technology Management | ||
| دوره 16، شماره 1، 2024، صفحه 98-117 اصل مقاله (1.34 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/jitm.2024.96377 | ||
| نویسندگان | ||
| Bhavani Rupa Devi1؛ Karthik Sagar Ashok2؛ Seemanthini Krishne Gowda3؛ Konatham Sumalatha4؛ Ganesan Kadiravan5؛ Ranjith Kumar Painam* 6 | ||
| 1Department of CSE Annamacharya Institute of Technology and Sciences, Tirupati, India. | ||
| 2Department of Information Science and Engineering, BMS Institute of Technology and Management, Bengaluru, Karnataka. | ||
| 3Department of Machine Learning (AI-ML) BMS College of Engineering, Bangalore, India. | ||
| 4Department of Database Systems, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore - 632014, Tamilnadu, India. | ||
| 5Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India. | ||
| 6Department of Electronics and Communication Engineering, Kallam Haranadhareddy Institute of Technology (Autonomous), NH-16, Chowdavaram, Guntur, Andhra Pradesh, India. | ||
| چکیده | ||
| 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. | ||
| کلیدواژهها | ||
| Lung Cancer؛ Convolutional Neural Network (CNN)؛ Histopathological Images؛ Transfer Learning؛ Lung Cancer Detection | ||
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