• ATHMAKURI NAVEEN KUMAR

A proper and timely diagnosis is essential for effective rehabilitation and treatment of leukaemia, a kind of cancer that can be deadly. Automated computer technologies have replaced traditional techniques for analysing, diagnosing, and forecasting symptoms. In this study, a comparison of two distinct leukaemia detection techniques was conducted. The techniques were a multi-class classification model, which used to be once an image-processing technique, and sequence of genomic approach, it is a binary classification mannequin. The enter values for the strategies varied. But for their neighbourhood design, they every employed convolutional neural networks (CNN). Additionally, they used 3-way cross-validation to divide their datasets. Learning curves, a confusion matrix, and a classification file have been the evaluation methods used to analyse the outcomes. The results verified that the genome mannequin carried out higher, with a total accuracy of 98% for a number of values that were accurately predicted. This result was contrasted with the findings of the image processing technique, which had a total accuracy value of 81%. The varying test results of the algorithms may be caused by the size of the various data sets.

A COMPARATIVE STUDY OF CANCER DETECTION MODELS USING ARTIFICIAL INTELLIGENCE

"A COMPARATIVE STUDY OF CANCER DETECTION MODELS USING ARTIFICIAL INTELLIGENCE", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 11, page no.g293-g313, November-2022, Available :http://www.jetir.org/papers/JETIR2211668.pdf

"A COMPARATIVE STUDY OF CANCER DETECTION MODELS USING ARTIFICIAL INTELLIGENCE", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 11, page no. ppg293-g313, November-2022, Available at : http://www.jetir.org/papers/JETIR2211668.pdf