CE-EEN-B0: Contour Extraction Based Extended EfficientNet-B0 for Brain Tumor Classification Using MRI Images

Abstract

A brain tumor is the uncharacteristic progression of tissues in the brain. These are very deadly, and if it is not diagnosed at an early stage, it might shorten the affected patient's life span. Hence, their classification and detection play a critical role in treatment. Traditional Brain tumor detection is done by biopsy which is quite challenging. It is usually not preferred at an early stage of the disease. The detection involves Magnetic Resonance Imaging (MRI), which is essential for evaluating the tumor. This paper aims to identify and detect brain tumors based on their location in the brain. In order to achieve this, the paper proposes a model that uses an extended deep Convolutional Neural Network (CNN) named Contour Extraction based Extended EfficientNet-B0 (CE-EEN-B0) which is a feed-forward neural network with the efficient net layers; three convolutional layers and max-pooling layers; and finally, the global average pooling layer. The site of tumors in the brain is one feature that determines its effect on the functioning of an individual. Thus, this CNN architecture classifies brain tumors into four categories: No tumor, Pituitary tumor, Meningioma tumor, and Glioma tumor. This network provides an accuracy of 97.24%, a precision of 96.65%, and an F1 score of 96.86% which is better than already existing pre-trained networks and aims to help health professionals to cross-diagnose an MRI image. This model will undoubtedly reduce the complications in detection and aid radiologists without taking invasive steps.

Classification of Brain Tumors Using Hybrid Feature Extraction Based on Modified Deep Learning Techniques

“…Automatic segmentation and diagnosis of brain cancer using deep-learning techniques can save lives and prevent further tumor growth. Numerous approaches, such as those in [1][2][3][4][5][6], were implemented to segment brain tumors using medical images, such as CT scans and MRIs. Nevertheless, various developed models provided an average accuracy between 90% and 98%.…”

“…In a study quite similar to this one, Mahesh et al in [4] developed a model to detect and classify BrT. Their study classified according to a tumor's location, identified using an extended Deep Convolutional Neural Network (DCNN) tool with feed-forward mode on MRIs.…”

“…Cancer, a term that encompasses any presence of one or many tumors, refers to the uncontrollable growth of cells inside the human body. This growth can be slow or aggressive [2][3][4][5]. This phenomenon occurs suddenly at any age without any noticeable symptoms [5,6].…”

“…Brain cancer detection has been studied for over a decade using various imaging techniques, such as CT scans and MRIs. Multiple studies have been performed to improve brain tumor diagnoses using different technologies [2,[4][5][6][7][8][9][10]. These technologies aim to save lives, improve patients' quality of life, or reduce the need for surgery.…”

“…These technologies aim to save lives, improve patients' quality of life, or reduce the need for surgery. Effective cancer treatments include chemotherapy, radiation, and surgery [1,4,[7][8][9][10][11][12].…”