2019/11/24 by Md Rashidul Hasan, Hasan, Md Rashidul, Muntasir Al Kabir +1 · 8 citations
Computer Science · Mathematics · Medicine · #Artificial intelligence #Cancer #Cancer detection #Computed tomography #Computer science #Data set #FOS: Computer and information sciences #Internal medicine #Lung #Lung Cancer Diagnosis and Treatment #Lung cancer #Lung cancer screening #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Medicine #Pathology #Pattern recognition (psychology) #Radiology #Radiomics and Machine Learning in Medical Imaging #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1911.10654
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2019/11/24 · arxiv created 2019/11/25 · arxiv updated 2019/11/26 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28
Lung cancer is one of the death threatening diseases among human beings.\nEarly and accurate detection of lung cancer can increase the survival rate from\nlung cancer. Computed Tomography (CT) images are commonly used for detecting\nthe lung cancer.Using a data set of thousands of high-resolution lung scans\ncollected from Kaggle competition [1], we will develop algorithms that\naccurately determine in the lungs are cancerous or not. The proposed system\npromises better result than the existing systems, which would be beneficial for\nthe radiologist for the accurate and early detection of cancer. The method has\nbeen tested on 198 slices of CT images of various stages of cancer obtained\nfrom Kaggle dataset[1] and is found satisfactory results. The accuracy of the\nproposed method in this dataset is 72.2%\n