• Mangalagiri Deepasai
• Vemulapati Pavani

In several medical vision implementations, segmentation and marking are also the weakest measures. This paper shows a system focused on watershed transformations, which are structured to solve popular problems in a number of applications and are controllable by parameter adaptation. Lung cancer identification, a system for segmenting cancer regions from CT images, a watershed algorithm for image segmentation, and brain tumour detection from MRI images are two of these modules introduced. Neural networks and Support Vector Machines are used to identify data utilising different GLCM features as well as certain mathematical features. We discuss the findings of both implementations in 2D MRI images of brain tumours and CT images of lung cancer to explain the algorithms' concepts and show their generic properties. Finally the rate of accuracy obtained is compared for ANN and SVM classification. The rate of accuracy is 100% for lung images and 96% for brain using SVM classifier. By experimental results the SVM performance is better and suitable in medical image classifications.

MRI Images, CT Images, GLCM, Neural Network, SVM

"Identification of Tumour in Lung and Brain using Segmentation Classification Technique", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 3, page no.846-857, March-2021, Available :http://www.jetir.org/papers/JETIR2103114.pdf

"Identification of Tumour in Lung and Brain using Segmentation Classification Technique", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 3, page no. pp846-857, March-2021, Available at : http://www.jetir.org/papers/JETIR2103114.pdf