Deep Learning-Based Lung Cancer Detection Using CT Scan Images: A Comparative Study of VGG16, ResNet50, and DenseNet121
Suhas Mohite1, Amol Kadam2, Sunil Kadam3, Chetan More4, Vinod Patil5, Sandip Chavan6
1Dr Suhas Mohite, Department of Pharmaceutical Chemistry, Bharati Vidyapeeth (Deemed to be University), Yashwantrao Mohite College of Arts, Science and Commerce, MS, Pune (Maharashtra), India.
2Dr Amol Kadam, Department of Computer Science and Business Systems, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune (Maharashtra), India.
3Prof. Sunil Kadam, Department of Mechanical Engineering, Bharati Vidyapeeth’s College of Engineering, Kolhapur (Maharashtra), India.
4Dr Chetan More, Department of Electronics and Telecommunication, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune (Maharashtra), India.
5Dr Vinod Patil, Department of Electronics and Telecommunication, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune (Maharashtra), India.
6Prof. Sandip Chavan, Department of Computer Engineering, Bharati Vidyapeeth’s College of Engineering, Navi Mumbai.
Manuscript received on 28 July 2026 | First Revised Manuscript received on 06 August 2026 | Second Revised Manuscript received on 10 August 2026 | Manuscript Accepted on 15 August 2026 | Manuscript published on 30 August 2026 | PP: 17-22 | Volume-15 Issue-9, August 2026 | Retrieval Number: 100.1/ijitee.J130115100926 | DOI: 10.35940/ijitee.J1301.15090826
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Lung cancer is a major problem because it kills more people every year than any other type of cancer. If we can find the abnormal areas early, people are much more likely to recover. Doctors usually use a type of scan called a CT scan to look for these abnormal areas. The problem is that reviewing all the scan images is very tedious, and even experienced doctors can miss something. We wanted to see whether computers could help with this task. We tried using three kinds of computer programs, VGG16, ResNet50 and DenseNet121, to look at the pictures from the scan. We used a collection of pictures called the LIDC-IDRI collection to teach the computers what to look for. DenseNet121 performed best at identifying the regions. We think this is because it is good at using the information it has to make decisions. We also used a tool called Grad-CAM to see what the computers were actually looking at. It showed that the better programs focused on the abnormal areas, not just the normal tissue. Our results show that using computers to help with this task is promising, and it could help doctors make better decisions. We also know there is still a lot of work to do to make it perfect.
Keywords: Lung Cancer Detection, CT Scan Analysis, Neural Network, Transfer Learning, VGG16, Resnet50, Densenet121, Grad-CAM, LIDC-IDRI Medical Image Classification
Scope of the Article: Computer Science and Engineering