• Himanshu Nama
• Suryatheja Alladi
• Saidesh Goje
• M.Nithin Reddy
• K.Haripriya

In modern times, it is evident that lung cancer remains the leading cause of cancer-related fatalities among patients. Currently, the primary diagnostic approach for suspected cases involves the examination of CT scan images of the lungs. Early detection is therefore crucial in preventing mortality. However, radiologists are often faced with limited time to analyze and interpret medical images, despite the increasing volume of imaging data generated by advancements in medical technology. Deep learning and machine learning have emerged as powerful technologies for automating the diagnosis and interpretation of medical images.In this study, we utilized biopsy reports that included symptoms such as hoarseness, coughing, smoking addiction, yellow fingers, anxiety, fatigue, allergy, wheezing, alcohol consumption, and breathing problems, displayed in patients' CT scans, to determine the cancer nodules as well as the stage of lung cancer. Additionally, we developed a website that leverages segmentation using watershed and a VGG16 CNN model to analyze CT scan results and generate a comprehensive medical profile for patients. By incorporating these sophisticated models, we can improve our predictive capabilities, and the majority of the image analysis process is automated through the use of machine learning and deep learning models. As a result, medical professionals can deliver prophylaxis more efficiently.

Lung Cancer, Image PreProcessing and Segmentation, Boundary detection, CNN, Computed Tomography, Visual Geometry Group(VGG16)

"Lung Cancer Detection from CT Scan using Watershed Image Segmentation and VGG16 Convolutional Neural Network", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.e337-e341, May-2023, Available :http://www.jetir.org/papers/JETIR2305451.pdf

"Lung Cancer Detection from CT Scan using Watershed Image Segmentation and VGG16 Convolutional Neural Network", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. ppe337-e341, May-2023, Available at : http://www.jetir.org/papers/JETIR2305451.pdf