RETRACTED ARTICLE: Computer aided diagnosis of brain tumor using novel classification techniques

Paul, Jasmine; Sivarani, T. S.

doi:10.1007/s12652-020-02429-6

Cited by 18 publications

(9 citation statements)

References 13 publications

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“…https://www.indjst.org/ 4 a comparison of the accuracy achieved by the LeakyReLU and ReLU with previously evaluated results. The testing accuracy of the proposed models are noticeably higher than those achieved by the models listed in the references (17)(18)(19)(20)(21) . (19) Fuzzy logic Two 450 -95.67% -Vankdothu et al (20) RCNN Three 264 PYTHON 95.17% -Gu et al (21) CNN The improved performance is achieved due to the fine-tuning of the model's hyperparameter values, optimizer type, batch and kernel sizes, activation functions, pool size, the number of neurons used in the convolution layers, and the number of training epochs.…”

Section: Resultsmentioning

confidence: 64%

MRI Brain Tumor Prediction using Azure Streamlit Framework and Analysis of CNN Activation Functions

Saxena,

Singh,

Singh

2023

IJST

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Objective:The present research work is focused on brain tumor classification, prediction and to increase the performance to locate the tumor region. Methods: A two-dimensional Convolutional Neural Network (CNN) model is proposed to classify the Magnetic Resonance Images (MRI) into tumor and nontumor categories. The method is applied on a collected dataset consisting of 2056 MRI images. The model is implemented in Python with hyperparameter tuning and activation functions.Findings: In this paper, ReLU and LeakyReLU activation functions are applied with several optimizers. The analysis of the implemented results has been used to gauge performance accuracy. The computed results achieve 99.51% accuracy for predicting the brain tumor using LeakyReLU with Adam optimizer. Novelty: The proposed model provides quick, and accurate approach to classify patients by setting hyperparameter tuning parameters which helps to the doctor to detect patients suffering with tumor and the entire process reduces the computation time.

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Section: Resultsmentioning

confidence: 64%

MRI Brain Tumor Prediction using Azure Streamlit Framework and Analysis of CNN Activation Functions

Saxena,

Singh,

Singh

2023

IJST

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“… Sumathi and Mandadi (2021) developed an automated segmentation approach using kernel-based probabilistic C-means (KPCM), particle swarm optimization (PSO) and morphological operations. Paul and Sivarani (2020) designed a CAD approach to detect MR-based brain tumors using fuzzy K-means clustering (FKM), gray-level co-occurrence matrix (GLCM), and a bag of visual word (BOVW) classifier. Lu et al (2018) proposed a novel framework for the early detection of brain tumors using wavelet energy features and a kernel-based extreme learning machine (K-LEM).…”

Section: Introductionmentioning

confidence: 99%

Design of a medical decision-supporting system for the identification of brain tumors using entropy-based thresholding and non-local texture features

Reddy

Batchu²,

Polinati³

et al. 2023

Front. Hum. Neurosci.

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IntroductionBrain tumors arise due to abnormal growth of cells at any brain location with uneven boundaries and shapes. Usually, they proliferate rapidly, and their size increases by approximately 1.4% a day, resulting in invisible illness and psychological and behavioral changes in the human body. It is one of the leading causes of the increase in the mortality rate of adults worldwide. Therefore, early prediction of brain tumors is crucial in saving a patient’s life. In addition, selecting a suitable imaging sequence also plays a significant role in treating brain tumors. Among available techniques, the magnetic resonance (MR) imaging modality is widely used due to its noninvasive nature and ability to represent the inherent details of brain tissue. Several computer-assisted diagnosis (CAD) approaches have recently been developed based on these observations. However, there is scope for improvement due to tumor characteristics and image noise variations. Hence, it is essential to establish a new paradigm.MethodsThis paper attempts to develop a new medical decision-support system for detecting and differentiating brain tumors from MR images. In the implemented approach, initially, we improve the contrast and brightness using the tuned single-scale retinex (TSSR) approach. Then, we extract the infected tumor region(s) using maximum entropy-based thresholding and morphological operations. Furthermore, we obtain the relevant texture features based on the non-local binary pattern (NLBP) feature descriptor. Finally, the extracted features are subjected to a support vector machine (SVM), K-nearest neighbors (KNN), random forest (RF), and GentleBoost (GB).ResultsThe presented CAD model achieved 99.75% classification accuracy with 5-fold cross-validation and a 91.88% dice similarity score, which is higher than the existing models.DiscussionsBy analyzing the experimental outcomes, we conclude that our method can be used as a supportive clinical tool for physicians during the diagnosis of brain tumors.

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“…However, early diagnosis is crucial for patients, and failure to provide one within a short time period could cause physical and financial discomfort [8]. To minimize these inconveniences, computer-aided diagnoses (CADs) can be used to detect BTs using multi-class and binary-class BT-MRI images [11]. The CAD system assists radiologists and neurologists in comprehensively interpreting, analyzing, and evaluating BT-MRI data within a short time period [12,13].…”

Section: Introductionmentioning

confidence: 99%

Explainable Deep Learning Approach for Multi-Class Brain Magnetic Resonance Imaging Tumor Classification and Localization Using Gradient-Weighted Class Activation Mapping

Hussain,

Shouno

2023

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Brain tumors (BT) present a considerable global health concern because of their high mortality rates across diverse age groups. A delay in diagnosing BT can lead to death. Therefore, a timely and accurate diagnosis through magnetic resonance imaging (MRI) is crucial. A radiologist makes the final decision to identify the tumor through MRI. However, manual assessments are flawed, time-consuming, and rely on experienced radiologists or neurologists to identify and diagnose a BT. Computer-aided classification models often lack performance and explainability for clinical translation, particularly in neuroscience research, resulting in physicians perceiving the model results as inadequate due to the black box model. Explainable deep learning (XDL) can advance neuroscientific research and healthcare tasks. To enhance the explainability of deep learning (DL) and provide diagnostic support, we propose a new classification and localization model, combining existing methods to enhance the explainability of DL and provide diagnostic support. We adopt a pre-trained visual geometry group (pre-trained-VGG-19), scratch-VGG-19, and EfficientNet model that runs a modified form of the class activation mapping (CAM), gradient-weighted class activation mapping (Grad-CAM) and Grad-CAM++ algorithms. These algorithms, introduced into a convolutional neural network (CNN), uncover a crucial part of the classification and can provide an explanatory interface for diagnosing BT. The experimental results demonstrate that the pre-trained-VGG-19 with Grad-CAM provides better classification and visualization results than the scratch-VGG-19, EfficientNet, and cutting-edge DL techniques regarding visual and quantitative evaluations with increased accuracy. The proposed approach may contribute to reducing the diagnostic uncertainty and validating BT classification.

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RETRACTED ARTICLE: Computer aided diagnosis of brain tumor using novel classification techniques

Cited by 18 publications

References 13 publications

MRI Brain Tumor Prediction using Azure Streamlit Framework and Analysis of CNN Activation Functions

MRI Brain Tumor Prediction using Azure Streamlit Framework and Analysis of CNN Activation Functions

Design of a medical decision-supporting system for the identification of brain tumors using entropy-based thresholding and non-local texture features

Explainable Deep Learning Approach for Multi-Class Brain Magnetic Resonance Imaging Tumor Classification and Localization Using Gradient-Weighted Class Activation Mapping

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