Aug 2026· International Conference on Circuit, Power and Computing Technologies· pp. 586-591· 0 citations· 11 references
Abstract
Due to its late identification and challenging diagnosis, lung cancer continues to be one of the top causes of death for cancer patients globally, positioning it as one of the most critical concerns. Timely identification of cancerous nodules is essential for enhancing the patient’s survival likelihood CT image analysis by hand is not very productive and significantly relies on a specialist’s expertise. In this study, we offer an autonomous lung cancer classification method based on explainable deep learning. The popular DenseNet121 network serves as the foundation for our deep learning model, which is enhanced by the Convolutional Block Attention Module (CBAM). To improve feature extraction of significant spatial and channel properties of input data, attention techniques are added. Furthermore, our method is interpretable because the Grad-CAM technique makes it possible to explain the choices made by a machine learning system. A database of CT scans, comprising 4,598 pictures categorized by large cell carcinoma, adenocarcinoma, and healthy lungs, was utilized. Our evaluations show the model’s effectiveness with an accuracy rate of 94.6\%.
However, early-stage lung cancer is one of the major causes of death from cancer due to its symptoms being absent or hard to detect by conventional clinical analysis. The late diagnosis of this disease causes the effectiveness of the treatment to become poor and low survival rates. Traditional interpretation of medical...
R. Dhamotharan, T. Manikumar· International Conference Com...· 0 citations
One of the main causes of cancer-related fatalities globally is lung cancer, and increasing patient survival requires early diagnosis. Lung cancer screening frequently uses computed tomography (CT) imaging, although manual interpretation can be laborious and reliant on radiologist skill. A deep learning-based framework...
Belayet Hossen, Md Takbir Alam Manjar, Md Abu Shihab et al.· International Conference Com...· 0 citations
The integration of these advanced techniques significantly enhances diagnostic reliability, addressing the challenges in histopathological image analysis, and proposes a novel explainable multi-model DL framework for breast cancer classification leveraging histopathological images.
Muhammad Nabeel Mehmood, Muhammad Hassaan Ashraf· Informatica· 0 citations
Lung cancer happens to be one of the most significant causes of deaths throughout the globe, necessitating efficient diagnosis for patient survival and accurate treatment plans. Although deep learning approaches continue to develop, there is still scope for bettering the efficiency in feature extraction, lesion localiz...
P. Sivakrishna, K. Thinakaran· International Conference Com...· 0 citations
Lung cancer remains one of the leading causes of cancer-related deaths worldwide, and early identification of malignant abnormalities plays an important role in improving patient survival rates. However, accurate lung cancer classification using CT imaging remains challenging because of limited dataset availability, cl...
Bodicherla Siva Sankar, D. Natarajasivan, M. Reddy· Frontiers in Artificial Inte...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
