A NOVEL HYBRID APPROACH OF DEEP LEARNING NETWORK ALONG WITH TRANSFER LEARNING FOR BRAIN TUMOR CLASSIFICATION

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A.Afreen Habiba, et. al.

Abstract

Brain tumors occur mainly due to the abnormal and uncontrollable partitioning that happened in the cell. In recent times, the DL[deep learning] method has helped the medical field by diagnosing the medical image process of several disease types. Among which brain tumor-based medical image classification is the most common one. It is done through Deep NN[neural Networks], where the TL[transfer Learning] on the MR image for brain tumor image classification helps in improving the metrics of classification by developing image-level stratified and patient-level models. In this research, almost 3640 T1-based MR[Magnetic Resonance] images from 233 patients with different types of tumors such as the [glioma, meningioma, pituitary] have been collected for research purpose. The image views are based on three views known as the sagittal, coronal, and axial views, respectively. The average brain image in all three views was found to be 14. Image classification is done with the help of cross-trained data with the use of the already trained Inception  V3-model. In the MR imagimg dataset, the 3-image-level and 1-patient-level have been a model to proceed with the process. The evaluation process during image classification is based on loss, accuracy, recall, kappa, precision, and AUC. The validation process is done through 4 methods: 10-fold cross-validation, holdout validation, group 10-fold cross-validation, stratified 10fold cross-validation. With the use of the cropped and uncropped dataset, the capacity of the generialization and its improvement process is detected. A siginificant result was gained with the utilization of 10-fold cross-validation, where it acquired an accuracy of 99.82%. The brain tumor classification from the MR image can be done with the help of DNN along with the transfer learning process. Our patient-level organization model noticed the best outcomes in arrangement to improve exactness.

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