1 Department of Electronics & Communication Engineering, Guru Nanak Dev Engineering College, Ludhiana, Punjab, India
Brain tumors are fatal diseases that are spread worldwide and affect all types of age groups. Due to its direct impact on the central nervous system, if tumor cells prevail at certain locations in the brain, the overall functionality of the body is disturbed and chances of a person approaching death are high. Tumors can be cancerous or non-cancerous but in many cases, the chances of complete recovery are less and as a result death rate has increased all over the world despite recent advancements in technology, equipment and awareness. So the main concern is to detect brain related diseases at early stages so that they do not spread into vital parts of brain and disrupt body functions. Also, more precise and accurate technologies are required to serve as aid in the diagnosis, treatment and surgery of brain.
Aims & Objectives:
Therefore, its high alarming time to monitor mortality statistics and develop faster and accurate methods to curb the situation by simulating tissue deformation and locating cancerous nodes which is currently the prominent area of interest.
A brain tumor is used to design the deformation model. Early stage detection of tumors is difficult from images. Moreover, the accuracy involved is low. Keeping all this into consideration, a machine learning approach has been developed for classification of cancerous and non-cancerous tissues so that the tissues having risk of future problem can also be recognized. The patient’s deformation model can be designed and brain tumor patterns are given as input on the basis of which tumor in the brain is marked. The proposed method of segmentation is based on a statistical model called Hidden Markov Model (HMM) which extricates the cancerous portion out of fed input MRI image along with the calculation of parameters such as Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), fault rate dust detection and accuracy.
The results obtained from parametric analysis show that HMM has performed better than the technique of Support Vector Regression (SVR) for brain cancer segmentation in terms of PSNR, MSE, fault rate dust detection and accuracy. So image processing is used in combination with Hidden Markov Model for classification and analysis to which MRI images are fed as input.
In this way, integration of artificial intelligence techniques with image processing can serve as a good way for segmentation of tumors and for classification purposes with good accuracy.
Keywords: Brain cancer, Segmentation, Classification, Hidden Markov Model (HMM), Support Vector Regression(SVR), Accuracy.
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