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Lung Cancer Detection using Deep Learning

1Mohammed Aamir Sharieff D, 2Parthiban R, 3Prem Kumar M, 4Mrs. C. Subalakshmi 5Dr. T. Kumanan, 6Dr. M. Nisha. 1,2,3 Students, Department of CSE4,6 Assistant Professor, Department of CSE 5 Professor, Department of CSE
Dr.M.G.R Educational and Research Institute, Maduravoyal, Chennai 95, Tamilnadu, India.

Abstract— Lung cancer is still ranked as one of the most common causes of cancer related deaths all over the world, and this has necessitated early detection which will enhance the survival chances of patients. The process of interpreting the images of lung CT scan by hand is a complicated and time- consuming process and it relies heavily on the experience of radiologists. This weakness underscores the importance of having effective automated machinery that can help in correct and effective diagnosis. In this contribution, a smart deep learning-based system of lung cancer identification was created. The proposed system was designed to take the raw DICOM CT images and perform normalization of Hounsfield Unit (HU) to maintain critical information on radiology. The preprocessing of the images followed by noise reduction, intensity normalization and resizing of the images was done to enhance the image quality overall. The hybrid models of U-Net and SegNet architectures were used to segment the tumor regions, which permitted a more accurate reflecting of the affected region through the combination of both contextual and boundary-level features.
Keywords: CT Scan Analysis , Deep Learning , Hybrid Segmentation , Image Preprocessing , Lung Cancer Detection , Medical Image Classification , SegNet , U-Net