A Brain Tumor Identification and Classification Using Deep Learning based on CNN-LSTM Method

Vankdothu, Ramdas; Hameed, Mohd Abdul; Fatima, Husnah

doi:10.1016/j.compeleceng.2022.107960

Cited by 94 publications

(28 citation statements)

References 13 publications

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“…Shahriar Mohammadi et al [31] introduced deep learning as a method for determining the finest qualities. The data is first normalized using the z-score.…”

Section: Literature Reviewmentioning

confidence: 99%

A model for multi-attack classification to improve intrusion detection performance using deep learning approaches

Silivery,

Rao Kovvur,

Solleti

et al. 2023

Measurement: Sensors

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“…Shahriar Mohammadi et al [31] introduced deep learning as a method for determining the finest qualities. The data is first normalized using the z-score.…”

Section: Literature Reviewmentioning

confidence: 99%

A model for multi-attack classification to improve intrusion detection performance using deep learning approaches

Silivery,

Rao Kovvur,

Solleti

et al. 2023

Measurement: Sensors

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“…They combined a CNN with a Long Short-Term Memory (STM) to extract the main brain features. Finally, the images have been classified and scored 89.39% accuracy in CNN, 90.02% in recurrent neural network (RNN), and 92% in the CNN-LSTM method [7].…”

Section: Related Workmentioning

confidence: 99%

An Improved Deep Structure for Accurately Brain Tumor Recognition

Ata¹,

Yousef²,

Karim³

et al. 2023

Computer Systems Science and Engineering

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Brain neoplasms are recognized with a biopsy, which is not commonly done before decisive brain surgery. By using Convolutional Neural Networks (CNNs) and textural features, the process of diagnosing brain tumors by radiologists would be a noninvasive procedure. This paper proposes a features fusion model that can distinguish between no tumor and brain tumor types via a novel deep learning structure. The proposed model extracts Gray Level Co-occurrence Matrix (GLCM) textural features from MRI brain tumor images. Moreover, a deep neural network (DNN) model has been proposed to select the most salient features from the GLCM. Moreover, it manipulates the extraction of the additional high levels of salient features from a proposed CNN model. Finally, a fusion process has been utilized between these two types of features to form the input layer of additional proposed DNN model which is responsible for the recognition process. Two common datasets have been applied and tested, Br35H and FigShare datasets. The first dataset contains binary labels, while the second one splits the brain tumor into four classes; glioma, meningioma, pituitary, and no cancer. Moreover, several performance metrics have been evaluated from both datasets, including, accuracy, sensitivity, specificity, F-score, and training time. Experimental results show that the proposed methodology has achieved superior performance compared with the current state of art studies. The proposed system has achieved about 98.22% accuracy value in the case of the Br35H dataset however, an accuracy of 98.01% has been achieved in the case of the FigShare dataset.

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“…To construct our models, we first applied a 23-layer convolution neural network (CNN) to the first dataset, which contained a significant number of MRI images for training. Ramdas Vankdothu et al’s [ 23 ] primary objective was to create an internet of things (IoT) computing system based on deep learning for identifying brain cancers in MRI images. This research proposed that by combining a CNN (convolutional neural network) with an LSTM (long short-term memory), CNN’s capacity to extract features may be enhanced.…”

Section: Related Workmentioning

confidence: 99%

Grade Classification of Tumors from Brain Magnetic Resonance Images Using a Deep Learning Technique

et al. 2023

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To improve the accuracy of tumor identification, it is necessary to develop a reliable automated diagnostic method. In order to precisely categorize brain tumors, researchers developed a variety of segmentation algorithms. Segmentation of brain images is generally recognized as one of the most challenging tasks in medical image processing. In this article, a novel automated detection and classification method was proposed. The proposed approach consisted of many phases, including pre-processing MRI images, segmenting images, extracting features, and classifying images. During the pre-processing portion of an MRI scan, an adaptive filter was utilized to eliminate background noise. For feature extraction, the local-binary grey level co-occurrence matrix (LBGLCM) was used, and for image segmentation, enhanced fuzzy c-means clustering (EFCMC) was used. After extracting the scan features, we used a deep learning model to classify MRI images into two groups: glioma and normal. The classifications were created using a convolutional recurrent neural network (CRNN). The proposed technique improved brain image classification from a defined input dataset. MRI scans from the REMBRANDT dataset, which consisted of 620 testing and 2480 training sets, were used for the research. The data demonstrate that the newly proposed method outperformed its predecessors. The proposed CRNN strategy was compared against BP, U-Net, and ResNet, which are three of the most prevalent classification approaches currently being used. For brain tumor classification, the proposed system outcomes were 98.17% accuracy, 91.34% specificity, and 98.79% sensitivity.

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A Brain Tumor Identification and Classification Using Deep Learning based on CNN-LSTM Method

Cited by 94 publications

References 13 publications

A model for multi-attack classification to improve intrusion detection performance using deep learning approaches

A model for multi-attack classification to improve intrusion detection performance using deep learning approaches

An Improved Deep Structure for Accurately Brain Tumor Recognition

Grade Classification of Tumors from Brain Magnetic Resonance Images Using a Deep Learning Technique

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