Computer aided diagnosis system for early detection of cancer in red blood cells

Abstract

Image processing techniques are now used in a variety of medical fields, allowing for better disease detection and classification. while processing the image, the response time is the main factor. different types of cancers are blood cancer, lung cancer etc. blood cancer is the most important cause of the rise in the death rate in the world. the blood cancer death rate, which was estimated by the national cancer institute in 2018, was 6, 09,640. blood cancer seriously affects almost 75 percent of people. the terms rbcs, red blood corpuscles, and hamates are most commonly recognized by red blood cells. the oxygen from the lungs in the blood is taken up by rbc in the blood stream and thus released into tissues in the definite blood stream as it squeezes with the blood platelets through the capillaries in the body. the purpose of this study is mainly to define the cancerous area and to classify the ct images of red blood cell cancer. the overlapping of the pixels of the samples create improper clustering of cancer cell regions. this improper clustering creates boundary distortion, which creates improper segmentation with complex calculations. thus it is necessary to create a research model for proper segmentation and classification of blood cells. the purpose of this research is to establish a computer-aided diagnosis (cad) method to identify malignant and benign nodules in order to decide if the production of cad changes at an early and advanced stage in the detection of nodules associated with such cancer. a successful system for improving the diagnosis of radiologists has been shown to be the use of image processing techniques and computer assisted diagnosis (cad) systems, especially in the case of medical image processing. the main objective of this research is to estimate the efficacy of the nodule detection process of computer-aided detection (cad) in computed tomography screening (ct).red blood cell cancer ct images are obtained from bccd (blood cell count and detection). with the help of matlab 2018a, the