Cascaded regression with dual CNN frame work for time effective detection of gliomas cancers

Abstract

The determination of brain tumor growth primarily relies on the histopathological examination of biopsy samples. Tumor segmentation in the brain presents a significant challenge in medical image analysis due to its complexity. The ultimate goal is to accurately identify and isolate tumor regions. For the segmentation of brain tumors, a variety of deep-learning techniques have been developed, and they have produced promising results. However, achieving accurate segmentation requires the integration of multiple image modalities with varying contrasts. This makes manual segmentation impractical for larger studies, despite its accuracy. Deep learning's exceptional performance has made it an attractive method for quantitative analysis. The field of medical image analysis presents distinctive challenges that must be overcome to achieve optimal results. The ongoing strategy is obtrusive, tedious and inclined to manual mistakes. These weaknesses show that it is so fundamental to play out a completely computerized technique for the multi-characterization of cerebrum cancers in view of deep learning. Thus, this paper presents an efficient time-optimized and deep-learning model based on cascade regression (DLCR) to segment the tumor grade in the following stages: Data Acquisition in which data were obtained from the well-known brain repository BRATS2017, which included 215 HGG (High-Grade Gliomas) and 80 LGG (Low-Grade Gliomas) glioma cases. Fully Convolutional Neural Network (FCNN) preprocessing was used to remove noise and anomalies from the raw data, and Gaussian Mixture Model feature extraction was used to extract features from the preprocessed image and finally the proposed DLCR model for grade identification. Experimental findings indicate that the suggested system surpasses other pre-existing models in various aspects (accuracy: 0.96, sensitivity:0.97, precision:0.88).