A deep learning based system for accurate diagnosis of brain tumors using T1-w MRI

Ahmed, Mona; Khalifa, Fahmi; Moustafa, Hossam El-Din; Saleh, Gehad A.; AbdElhalim, Eman

doi:10.11591/ijeecs.v28.i2.pp1192-1202

Cited by 7 publications

(4 citation statements)

References 21 publications

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“…Even in veterinary medicine, artificial intelligence was used to overcome the challenges that life faced in previous eras. With the training of convolutional neural networks (CNN) with the classification ResNet50, and reaching an accuracy of 99.8 %, with an error rate of 0.005 [6][7][8], deep learning found its way into a variety of fields, including security and medical fields, such as face recognition as well as recognition, and medical fields, such as brain tumors. In the recent past, after the Coronavirus, which causes lung inflammation and death, where an accuracy of 97.5 % was attained by providing an approach in medical images classification to diagnose the disease [9], Deep learning and a convolutional neural network are used to diagnose fractures in human bones.…”

Section: Introductionmentioning

confidence: 99%

Improvement of color image analysis using a hybrid artificial intelligence algorithm

Muhi-Aldeen,

Mahmood,

Abdulrahman

et al. 2024

Eureka: PE

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Large communications of voice and images over the Internet, which leads to limited space for very large data during the aforementioned correspondence, to overcome this issue to maintain the quality of this technology. The proposal in the efficient algorithm in this work is a method to derive the two new filters from the second and third Chebyshev polynomials by forming the discrete wavelets with the mother wavelet to be used in image processing in order to overcome the problem mentioned above due to the correspondence, The filters that were derived are Filter Discrete Second Chebyshev Wavelets Transform (FDSCWT) and Filter Discrete Third Chebyshev Wavelets Transform (FDTCWT)to process the image by analysis, noise reduction, and image compression. Many of the techniques previously used in the field of image processing do not preserve image information during processing, but when using the new technology proposed in this work, it has been proven to preserve the image with its important information and data through the readings obtained shown in the tables below. These readings are average. Mean Square Error (MSE), Peak Signal Noise Ratio (PSNR), Bits Per Pixel (BPP), and Compression Ratio (CR) in preprocessing. After the initial processing stage, the deep learning stage begins in the field of artificial intelligence. A (CNN) is trained with the two new filters to be the first Discrete Second Chebyshev Wavelets (DSCWCNN) and the second Discrete Third Chebyshev Wavelets Convolutional Neural Network (DTCWCNN), with the code being generated in the MATLAB program with a network Alex Net to complete the classification process that was added in this work to implement the recognition technology. Faces detection with new filters in deep learning to be a unique experience to reach a high level of accuracy of 98.60 % with the network for the filter DSCWCNN and 98.92 % with the network for the filter DTCWCNN in a very short time, which will be mentioned later in the work

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Section: Introductionmentioning

confidence: 99%

Improvement of color image analysis using a hybrid artificial intelligence algorithm

Muhi-Aldeen,

Mahmood,

Abdulrahman

et al. 2024

Eureka: PE

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“…The role of artificial intelligence in the medical field as well as in many other fields is shown through deep learning in general and the convolutional neural network in particular, there has been interest in artificial intelligence and deep learning recently due to the large and massive amounts of data that need to be examined and knowledge of the sample being examined [1], [2]. Diab et al [3] deep learning and convolutional neural network training were used to detect brain tumors, especially ResNet50 was used, with an accuracy of 99.8% with an error of 0.005.…”

Section: Introductionmentioning

confidence: 99%

Kidney stones detection based on deep learning and discrete wavelet transform

Tahir¹,

Abdulrahman²

2023

IJEECS

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<p>The problem of the research is to find medical images of purity, high quality and free of impurities, which contributes to enabling doctors to obtain the results of analyzing the health status of each patient according to his disease data. Therefore, it was necessary to use discrete first chebysheve wavelets transform (DFCWT) technique in order to remove the associated impurities that appear in the medical images, and then analyze the results for all of the above, the algorithm DFCWT has been combined with and linking it to a neural network based on convolutional neural network (CNN) and this contributes to obtaining the results of analyzing image data with high accuracy and speed. The new algorithm proposed in this paper is based on deep learning finding the identification of kidney stones using DFCWT and the same process can be repeated on skin cancer, bones and fractures, processing by discrete first chebyshev wavelet transformation convolution neural network (DFCWTCNN).</p>

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“…The radiographic diagnosis may be more difficult because of superimpositions, poorly seen partial cortical loss, and difficulties interpreting flat and short bones and soft tissues [5]. Radiologists have expressed interest in using contemporary computer-aided diagnostic (CAD) tools to save time and effort [6]. We use a set of CT scans because of their excellent spatial and contrast resolution.…”

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confidence: 99%

“…To  ISSN: 2302-9285 prevent over-fitting, a global average pooling (GAP) layer was included in the design for the ResNet50 output. Finally, a sigmoid layer was used to obtain the classification [6]. Additionally, in 2022, the study presented new mathematical modeling that uses effective processing to evaluate and identify the features of artificial pulse audio signals.…”

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confidence: 99%

A deep learning-based system for accurate diagnosis of pelvic bone tumors

Shouman,

Rahouma,

Hamed

2024

Bulletin EEI

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Bone image analysis and categorizing bone cancers have both seen advancements thanks to deep learning (DL), more notably convolution neural networks (CNN). This study suggests a brand-new CNN-based methodology for categorizing pelvic bone tumors specifically. This work aims to create a pelvic bone computed tomography (CT) image categorization system based on deep learning. The proposed technique uses a convolutional neural network (CNN) architecture to automatically extract information from the CT images and classify them into distinct categories of tumors. A total of 178 3D CT pictures was discovered and added retroactively. DenseNet created the image-based model with Adam optimizer and cross entropy loss. The suggested system's accuracy is assessed using a variety of performance indicators, including sensitivity, specificity, and F1-score. As demonstrated by the experiment findings, the suggested deep learning based classification system has a high degree of accuracy (94%), making it useful for the diagnosis and treatment of pelvic bone tumors. Our promising results might hasten the use of DL-assisted CT diagnosis for pelvic bone tumors in the future.

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A deep learning based system for accurate diagnosis of brain tumors using T1-w MRI

Cited by 7 publications

References 21 publications

Improvement of color image analysis using a hybrid artificial intelligence algorithm

Improvement of color image analysis using a hybrid artificial intelligence algorithm

Kidney stones detection based on deep learning and discrete wavelet transform

A deep learning-based system for accurate diagnosis of pelvic bone tumors

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