A Hybrid CNN-SVM Threshold Segmentation Approach for Tumor Detection and Classification of MRI Brain Images

Khairandish, Mohammad Omid Khairandish Mohammad Omid; Sharma, Meenakshi; Jain, Vishal; Chatterjee, Jyotir Moy; Jhanjhi, N. Z.

doi:10.1016/j.irbm.2021.06.003

Cited by 268 publications

(81 citation statements)

References 35 publications

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“…In this paper, an analytical comparison among the proposed model and different explored models has been proceeded. An exploration has been presented between the proposed model and the other well-known deep convolutional neural network (DCNN) models such as VGG16 [19], VGG19 [19] and hybrid CNN-SVM [20]. These explored models were pre-trained with the standard ImageNet dataset [33] to get an initial weight, it has 1.2 million color images.…”

Section: Discussionmentioning

confidence: 99%

“…On the other hand, our proposed model uses 4 max-pooling layers only and it's a balanced number according to our results. • The explored models [18]- [20] were designed to deal small-sized images, so they configured their convolutional filter with 3×3 to be able to find small patterns. However, the brain tumor patterns are relatively large, so using a large filter size in our convolutional operations will be a better choice.…”

Section: Discussionmentioning

confidence: 99%

“…Their approach scored 91.02% in the accuracy metric. Khairandish et al [20] proposed a hybrid model based on a convolutional neural network (CNN) and SVM to detect brain tumors in the MRI images. They also applied a pre-processing approach on the MRI images, which hugely improves their accuracy score.…”

Section: Related Workmentioning

confidence: 99%

“…• An analytical detailed comparison of Glioma, Meningioma and Pituitary detection is conducted between the proposed architecture and well-known approaches including (VGG16 [18] and VGG19 [19]) and the recent approaches like CNN-SVM [20]. We organized the rest of the paper as follows: First section II discusses the related work, Secondly, section III describes the applied methodologies in this paper, Thirdly, section IV discusses the experimental results.…”

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confidence: 99%

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A New Convolutional Neural Network Architecture for Automatic Detection of Brain Tumors in Magnetic Resonance Imaging Images

2022

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Brain diseases are mainly caused by abnormal growth of brain cells that may damage the brain structure, and eventually will lead to malignant brain cancer. An early diagnosis to enable decisive treatment using a Computer-Aided Diagnosis (CAD) system has major challenges, especially accurate detection of different diseases in the magnetic resonance imaging (MRI) images. In this paper, a new Deep Convolutional Neural Network (DCNN) architecture is proposed for effective diagnosis of glioma, meningioma and pituitary. The architecture uses batch normalization for fast training with a higher learning rate and ease initialization of the layer weights. The proposed architecture is a computationally lightweight model with a small number of convolutional, max-pooling layers and training iterations. A demonstrative comparison between the proposed architecture and other discussed models in this paper is conducted. An outstanding competitive accuracy is achieved of 97.72% overall, 99% in detecting glioma, 98.26% in detecting meningioma, 95.95% in detecting pituitary and 97.14% in detecting normal images when tested on a dataset with 3394 MRI images. Experimental results prove the robustness of the proposed architecture which has increased the detection accuracy of a variety of brain diseases in a short time.INDEX TERMS brain tumors, deep convolutional neural network, image processing, MRI images I. INTRODUCTION

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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confidence: 99%

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A New Convolutional Neural Network Architecture for Automatic Detection of Brain Tumors in Magnetic Resonance Imaging Images

2022

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“…In Convolutional Neural Networks (CNN), the main goal is to classify each pixel into a fixed category class (Khairandish, Sharma, Jain, IRBM, & 2021. Each image is a matrix.…”

Section: Materials and Methods 21 Convolutional Neural Networkmentioning

confidence: 99%

Deep Learning Based Garbage Detection for Autonomous Garbage Collection Vehicles

Uzun¹,

KARACA²

2022

European Journal of Science and Technology

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With the rapid technological advances and the increasing human population, the need for more production has emerged and consumption has increased accordingly. As a result of this increased consumption, more garbage has been generated. Environmental pollution caused by these garbages emerges as a problem that people have to overcome both in Turkey and in the world. Many studies have been conducted to overcome this problem. Especially today, with the development of autonomous vehicles and artificial intelligence, the solutions using these technologies have increased. In this study, a new data set was created for autonomous garbage collection vehicles and a model was proposed in which these vehicles can be used. The data set was prepared with images of garbage with paper cups, which is one of the most polluting garbage, taken in different places, and images consisting of different garbage without paper cups. Paper cups were detected from these images with pre-trained Squenzenet, VGG-19 and GoogLeNet convolutional neural networks. The performance rate of the SquenzeNet, GoogLeNet and Vgg-19 networks used in the study was found as 97.77%, 96.44% 94.66%, respectively.

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Improving Performance With Feature Selection, Extraction, and Learning

Patil,

Shinde,

Singh

et al. 2024

Integrating Metaheuristics in Computer Vision for Real‐World Optimization Problems

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A Hybrid CNN-SVM Threshold Segmentation Approach for Tumor Detection and Classification of MRI Brain Images

Cited by 268 publications

References 35 publications

A New Convolutional Neural Network Architecture for Automatic Detection of Brain Tumors in Magnetic Resonance Imaging Images

A New Convolutional Neural Network Architecture for Automatic Detection of Brain Tumors in Magnetic Resonance Imaging Images

Deep Learning Based Garbage Detection for Autonomous Garbage Collection Vehicles

Improving Performance With Feature Selection, Extraction, and Learning

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