000			a
999			_c18460 _d18460
003			OSt
005			20221228131159.0
008			221228b xxu||||| |||| 00| 0 eng d
040			_aAIKTC-KRRC _cAIKTC-KRRC
100			_919390 _aKumbhar, Vinod B
245			_aNovel Method of CT Chest Image Segmentation and Analysis for Early Lung Cancer Detection
250			_aVol, 103(6), Dec
260			_aNew York _bSpringer _c2022
300			_a1875-1883p
520			_aLung cancer is one of the most prominent and deadly diseases across the world. However, early detection of this disease has proved very useful in increasing the survival rate of the affected patients. With the development of sophisticated image processing algorithms, computer-aided diagnosis (CAD) is evolving as one of the most efficient methods for detecting lung cancer. CT scanning technique is routinely applied for diagnosing lung cancer. These CT scan images are checked by radiologists who comment on the presence or absence of cancer. However, CT scan images of an early-stage cancer are very hard to be identified with naked eyes. In this scenario, the CAD plays a major role to assist the radiologists. The four steps of images processing techniques employed in CAD are—(1) reading the image, (2) preprocessing or filtering the image, (3) morphological processing that involves erosion, dilation, etc., and (4) detecting a cancerous region in an image and improving the cancerous region's status as a candidate for true or false positive status. The proposed method in this work uses efficient image segmentation algorithms that help to enhance the accuracy of the diagnosis.
650		0	_94623 _aElectrical Engineering
700			_919391 _aChavan, Mahesh S
773	0		_tJournal of the institution of engineers (India): Series B _x2250-2106
856			_uhttps://link.springer.com/article/10.1007/s40031-022-00808-5 _yClick here
942			_2ddc _cAR