Detection and diagnosis of brain tumors using deep learning convolutional neural networks

Gurunathan, Akila; Batri, K.

doi:10.1002/ima.22532

Cited by 37 publications

(10 citation statements)

References 17 publications

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“…Furthermore, the results suggest that, such an optimized CNN analysis system can also contribute to energy efficient x-ray procedures, by utilizing exact or less resources to achieve excellent image classification quality, needed for better breast cancer and other cancerous imaging analysis, diagnosis and treatments; which would also relate to the CNN architecture employed, where some of the most commonly used pre-trained CNN architectures for mammography are: (Alex-Net [96][97][98][99][100][101][102][103][104][105][106], VGG16 [107-09], etc. ).…”

Section: Breast Cancer Mammography Image Classification Using Cnn And...mentioning

confidence: 99%

Analytical Optimization of X-ray Mammography for Increased Benefits and Safety; using: Data Analytics, Electronics Engineering and Artificial Intelligence

Kalu¹

2023

J Health Statist Rep

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Objective: To analyse, understand and investigate common X-ray Mammography techniques, terminologies and properties; and how the X-ray Mammography systems can be optimized for a safer, more effective, more efficient, more energy efficient procedure for breast cancer diagnosis; using Deep Learning (DL)/ Artificial Intelligence (AI), Analytical, Electronic Engineering, tools and Methodologies. Methods: Using data and breast cancer image datasets from validated open source data stores; investigative and comparative analyses were carried out on common X-ray Mammography techniques in relation to breast cancer diagnosis and treatments for Women; as well as optimization analyses using Electronic engineering principles and Artificial Intelligence principles on the quality, intensity, radiation photon energy, HVL(Half Value Layer), Tube current-time product, Metal filters properties (e.g. K-edge energy cut off), and classification validation accuracy of the X-ray Mammography properties, procedure, processes and systems. The methodical and data-driven analyses were carried out using the following Data, Artificial Intelligence (AI) and Electronic Engineering, methodologies and algorithms: Data Analytics, Convolutional Neural Networks (CNN) in Machine Learning (ML) Engineering, and X-ray-Impedance Circuit Composite System Analysis

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Section: Breast Cancer Mammography Image Classification Using Cnn And...mentioning

confidence: 99%

Analytical Optimization of X-ray Mammography for Increased Benefits and Safety; using: Data Analytics, Electronics Engineering and Artificial Intelligence

Kalu¹

2023

J Health Statist Rep

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“…However, the error rate of the designed model was not minimized. Gurunathan A (2020) designed deep convolutional neural networks (CNNs) architecture in Gurunathan and Krishnan (2020) to identify the tumor from the brain images. However, the precise detection rate was not achieved.…”

Section: Introductionmentioning

confidence: 99%

An automatic and intelligent brain tumor detection using Lee sigma filtered histogram segmentation model

Kurian

Juliet

2022

Soft Comput

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Brain tumors are the second important origin of death worldwide. The early and exact identification of brain tumors is significant for the healing process. With accelerating diagnoses, medicine as well as pricing, quantum computing permits disruptive cases to providers. Quantum improved deep learning was especially significant to the sector. However, the conventional machine learning method faces main challenges to achieve accurate brain tumor detection with MRI images. Therefore, this paper proposes a novel technique called Lee sigma filtered histogram segmentation (LSFHS) for accurately detecting brain tumors with minimal time consumption. LSFHS technique is based on preprocessing, segmentation, feature extraction and classification. Input MRI image is preprocessed using adaptive Lee sigma filter in the first hidden layer to minimize noise significantly. In hidden layer 2, gray bimodal histogram segmentation is performed to partition a preprocessed image into a number of segments. Multiple features are extracted from the input image in the third hidden layer. Output layer uses the TanH activation function to match extracted features with disease features for detecting brain tumors. Experimental evaluation is carried out on factors, namely peak signal-to-noise ratio, tumor detection accuracy, error rate and tumor detection with a number of MRI images. The results illustrate LSFHS technique increases tumor detection accuracy by 14% and 25% faster tumor detection time, and reduces the error rate by 58% compared to state-of-the-art works. Qualitative and quantitative results illustrate that our proposed LSFHS technique attains greater performance than state-of-the-art methods. LSFHS technique is designed to detect brain tumors at an earlier stage with higher tumor detection accuracy and less time.

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“…This work used distance metrics to improve the efficacy rate 19 . Gurunathan and Krishnan implemented a method to detect tumor portions using neural networks 20 . Ghoneim et al developed a bounding box and image‐specific fine‐tuning‐based method for segmentation (BIFSeg) 21 .…”

Section: Introductionmentioning

confidence: 99%

“…19 Gurunathan and Krishnan implemented a method to detect tumor portions using neural networks. 20 Ghoneim et al developed a bounding box and image-specific fine-tuning-based method for segmentation (BIFSeg). 21 In this framework, a bounding box is implemented in the CNN network and based on the weighted loss function metrics, and image-specific finetuning, the tissues in the input image are extracted.…”

Section: Introductionmentioning

confidence: 99%

A novel approach for MR brain tumor classification and detection using optimal CNN‐SVM model

Ragupathy

Subramani

Arumugam

2022

Int J Imaging Syst Tech

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The segmentation of brain tumors in magnetic resonance imaging plays a significant role in the field of image processing. This process has high computational complexity when handled manually by clinical experts. The accuracy in classifying and segmenting the brain tumor depends on the radiologists' experience. The computer‐aided diagnosis‐based brain tumor segmentation approach is proposed to overcome the existing limitations. The proposed convolutional neural network and support vector machine approach consists of the following stages. In the preprocessing stage, unwanted noise and intensity inhomogeneity are suppressed using an anisotropic diffusion filter. Then, the features are extracted using the deep convolutional neural network, and based on the features; the input brain image is classified into normal or abnormal using a support vector machine classifier. The proposed method gives a more successful accuracy rate of 2.11%. Compared with the other methods, the sensitivity and specificity values are also improved to 4.79% and 1.19%.

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Detection and diagnosis of brain tumors using deep learning convolutional neural networks

Cited by 37 publications

References 17 publications

Analytical Optimization of X-ray Mammography for Increased Benefits and Safety; using: Data Analytics, Electronics Engineering and Artificial Intelligence

Analytical Optimization of X-ray Mammography for Increased Benefits and Safety; using: Data Analytics, Electronics Engineering and Artificial Intelligence

An automatic and intelligent brain tumor detection using Lee sigma filtered histogram segmentation model

A novel approach for MR brain tumor classification and detection using optimal CNN‐SVM model

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