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Jain, Anjali
- Brain Tumor Classification Through MRI Images Using DenseNet169 – Statistical Evaluation
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Authors
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1 Research Scholar, USICT, GGSIPU, New Delhi, IN
2 Assistant Professor, CSED, Thapar Institute of Engineering and Technology, Punjab, IN
1 Research Scholar, USICT, GGSIPU, New Delhi, IN
2 Assistant Professor, CSED, Thapar Institute of Engineering and Technology, Punjab, IN
Source
Research Cell: An International Journal of Engineering Sciences, Vol 35, No SP (2023), Pagination: 233-252Abstract
According to world statistics, Cancer is the 10th leading cause of death in both men and women. Adding to this, the worldwide incidence rate of brain tumors was marked as 3.5 per 100,000 population(s) in 2020. One of the major reasons behind this alarming statistics is the high probability of tumors going undetected through the medical imaging practices in earlier stages of tumor development. This particular fact has drawn the attention of researchers working in the medical field to devise rapid and accurate diagnostic methods to reduce the mortality rate from brain tumors through identification at nascent stage and providing the requisite treatment. As digital image processing paves its way through Computer aided diagnostic in the medical field, exploring utility of deep learning techniques in medical image processing is a promising research avenue. This paper takes this cue to statistically evaluate DenseNet-169, a model based on DenseNet architecture as defined under Convolution Neural Network (CNN) for classifying Brain MRI images on the basis of presence or absence of brain tumor. Densenet-169 model along with a total of eight optimizers have been applied on publicly available brain tumor datasets to assess the performance of the said model in brain tumor classification. The performance of the model has been studied using a total of seven metrics and results obtained have been compared with the previous works in this field. The results obtained aid in concluding that DenseNet-169 with AdaDelta or AdaMax as the optimizer provide better results from the previous studies made. This in turn shall help in establishing DenseNet 169 as a model that can be effectively employed for tumor classification and extend the undertaken study further.Keywords
Brain Tumor, CAD, CNN, DenseNet, Deep Learning, MRI, Medical Image Processing.Full Text
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