A Brain Tumor Identification and Classification Using Deep Learning based on CNN-LSTM Method. (July 2022)

• Record Type: Journal Article
• Authors: Vankdothu, Ramdas; Hameed, Mohd Abdul; Fatima, Husnah
• Is Part Of: Computers & electrical engineering. Volume 101(2022)
• Publication Date: 2022-07
• Subjects: A Brain Tumor -- Convolutional Neural Network(CNN) -- Classification -- Deep Learning -- Magnetic Resonance Imaging -- LSTM; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.107960
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Brain tumors are one of the most often diagnosed malignant tumors in persons of all ages. Recognizing its grade is challenging for radiologists in health monitoring and automated determination; however, IoT can help. It is critical to detect and classify contaminated tumor locations using Magnetic Resonance Imaging (MRI) images. Numerous tumors exist, including glioma tumor, meningioma tumor, pituitary tumor, and no tumor (benign). Detecting the type of tumor and preventing it is one of the most challenging aspects of brain tumor categorization. Numerous deep learning-based approaches for categorizing brain tumors have been published in the literature. A CNN (Convolutional Neural Network), the most advanced method in deep learning, was used to detect a tumor using brain MRI images. However, there are still issues with the training procedure, which is lengthy. The main goal of this project is to develop an IoT computational system based on deep learning for detecting brain tumors in MRI images. This paper suggests combining A CNN(Convolutional Neural Network) with an STM(Long Short Term Memory), LSTMs can supplement the ability of CNN to extract features. When used for image classification, the layered LSTM-CNN design outperforms standard CNN classification. Experiments are undertaken to forecast the proposed model's performance using the Kaggle data set, which contains 3264 MRI scans. The dataset is separated into two sections: 2870 photos of training sets and 394 images of testing sets. The experimental findings demonstrate that the proposed model outperforms earlier CNN and RNN models in terms of accuracy.