Lung Cancer Malignancy Detection System on CT Scan using Region-Based Mask Convolutional Neural Network and Support Vector Machine Methods

Safi'i, Mochammad Imam (2021) Lung Cancer Malignancy Detection System on CT Scan using Region-Based Mask Convolutional Neural Network and Support Vector Machine Methods. Undergraduate thesis, Universitas 17 Agustus 1945 Surabaya.

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Abstract

The rapid development of information technology goes hand in hand with the development of digital image processing. Utilization of digital image processing has reached various fields, one of which is in the medical field. There are so many uses for digital image processing, from detecting tumors in the body to other internal diseases. Lung cancer is one of the most common cancers, with more than 225,000 cases, 150,000 deaths and more than 12 billion US dollars in cancer treatment costs each year in the United States. CT Scan is a medical examination procedure using a combination of X-ray technology and a special computer system to see conditions in the body from various angles and CT Scan pieces are considered accurate in taking pictures of nodules in the lungs. This study aims to detect lung cancer from CT scan images and classify lung cancer malignancies into three types of nodules, namely large benign, small benign, and malignant. The location of the nodules was detected by applying the Faster R-CNN method, and then segmented using the Segnet network. The system is able to identify and segment the nodule area with benign type very well, but has not succeeded in detecting and segmenting nodules with malignant type.

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