Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/330420
Title: Bio Medical Image Segmentation and Cross Validation for the Detection of Lung Cancer by Using Machine Learning
Researcher: Singh, Paramjit
Guide(s): Mahajan, Aprana N and Nanglia Pankaj
Keywords: Engineering
Engineering and Technology
Engineering Electrical and Electronic
University: Maharaja Agrasen University
Completed Date: 2021
Abstract: Lung cancer detection using Artificial Intelligence technique is becoming one of the most famous research areas. This is due to increase in the death rate due to lung cancer. The incidence and humanity rate of lung cancer rank first among various cancers across the world. Soft computing can employ a variety of feature extraction, feature optimization and classification techniques on the biomedical images so as to cure the disease at an early stage. newlineIn current scenario soft computing has been observed in playing an important role, especially in the diversified domain of medical sector. Soft Computing is nothing but it is computational intelligence that provides non-invasive techniques to diagnose the disease without harming the patient. Recent statistics observes that the detection and classification of lung cancer disease are one of the most tiresome tasks in the field of medical area. In the diversified segment of medical industry technology usage plays a very important role. The most challenging task is detection and diagnosis of the lung cancer at an early stage with more accuracy. In this Research work an efficient feature extraction and optimization technique has been implemented on 1000 sets of CT images. The performance of these techniques has been analysed on the basis of parameter minimum execution time with the minimum error rate. The main task of soft computing is to train the data set and classify the data set according to the requirement. newlineThe objective of this research work is to develop an effective and efficient cancer detection technique that can have a high level of accuracy and performance. The Comparative investigation of different feature extraction techniques is carried out for lung cancer detection system as a result an effective feature extraction technique could be identified. newlineA comparative analysis of the techniques, the Accuracy of PSO, ABC, FFA and CS is 97.14, 96.12, 95.07 and 98.14 respectively So, here it has been concluded that on the basis of above mentioned results Cuckoo search
Pagination: 
URI: http://hdl.handle.net/10603/330420
Appears in Departments:Maharaja Agrasen Institute of Technology



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