Medical Image Enhancement to Extract Brain Tumors from CT and MRI images

Al-Dabbas, Hadeel Moutaz; Morad, Ameer Hussian

doi:10.24996/ijs.2019.60.8.19

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…Accuracy is the most frequently used metric. The percentage of samples that can be accurately classified from all the data is known as classification accuracy [26]. However, variations computed between real (actual) and predicted classes' measures by CM give the following phrase nominations: false positives, true positives, false negatives, and true negatives that are determined as follows: True positive (TP) = the proportion of positive cases that were correctly identified.…”

Section: Performance Metricsmentioning

confidence: 99%

Classification of Brain Tumor Diseases Using Data Augmentation and Transfer Learning

Al-Dabbas,

Mohammed Salih

2024

Iraqi Journal of Science

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An accumulation of aberrant cells called a brain tumor is the outcome of unregulated cell division. Brain cancers can be found using magnetic resonance imaging (MRI). The exponential growth of deep learning networks has allowed us to tackle complex tasks, even in fields as complicated as medicine. However, using these models requires a large corpus of data for the networks to be highly generalizable and have high performance. This dearth of training data makes it critical to explore methods such as data augmentation. In this sense, data augmentation methods are widely used in strategies to train networks, and with small data sets being vital in medicine due to the limited access to data, this work aims to identify the best classification system by considering the prediction accuracy in this vein. Data augmentation is performed on the database and fed into the three convolutional neural network (CNN) models. A comparison line is drawn between the three models based on accuracy and performance on the Inception v3 models, Mobile Net V2, and Squeeze Net network for brain tumor detection and classifying 350 brain MR images. The statistical methods were modified in order to evaluate these algorithms. With 0.992% accuracy, 0.993% recall, 0.989% precision, and 0.994% F1 score, the Squeeze Net model performed the best. The Mobile Net V2 model, which had an accuracy of 0.964%, came next. When the research's findings were compared to those of related studies in the literature, they revealed better success rates than those of the majority of investigations.

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Section: Performance Metricsmentioning

confidence: 99%

Classification of Brain Tumor Diseases Using Data Augmentation and Transfer Learning

Al-Dabbas,

Mohammed Salih

2024

Iraqi Journal of Science

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“…Gliomas are a type of brain tumor characterized by the presence of glass cells as their predominant structural component. These tumors are frequently observed within the central nervous system (Al-Dabbas, et al, 2019;Al Mahmud, et al, 2020;Mohammed, et al, 2021).…”

Section: Classification and Segmentation Of Brain Tumorsmentioning

confidence: 99%

Brain Tumor Segmentation Using Enhancement Convolved and Deconvolved CNN Model

Almukhtar,

Morad,

Hussein

et al. 2024

ARO

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The brain assumes the role of the primary organ in the human body, serving as the ultimate controller and regulator. Nevertheless, certain instances may give rise to the development of malignant tumors within the brain. At present, a definitive explanation of the etiology of brain cancer has yet to be established. This study develops a model that can accurately identify the presence of a tumor in a given magnetic resonance imaging (MRI) scan and subsequently determine its size within the brain. The proposed methodology comprises a two-step process, namely, tumor extraction and measurement (segmentation), followed by the application of deep learning techniques for the identification and classification of brain tumors. The detection and measurement of a brain tumor involve a series of steps, namely, preprocessing, skull stripping, and tumor segmentation. The overfitting of BTNet-convolutional neural network (CNN) models occurs after a lot of training time because training the model with a large number of images. Moreover, the tuned CNN model shows a better performance for classification step by achieving an accuracy rate of 98%. The performance metrics imply that the BTNet model can reach the optimal classification accuracy for the brain tumor (BraTS 2020) dataset identification. The model analysis segment has a WT specificity of 0.97, a TC specificity of 0.925914, an ET specificity of 0.967717, and Dice scores of 79.73% for ET, 91.64% for WT, and 87.73% for TC.

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Brain Tumor Segmentation Utilizing Thresholding and K-Means Clustering

Khilkhal

Ismael

2022

2022 Muthanna International Conference on Engineering Science and Technology (MICEST)

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Medical Image Enhancement to Extract Brain Tumors from CT and MRI images

Cited by 3 publications

References 8 publications

Classification of Brain Tumor Diseases Using Data Augmentation and Transfer Learning

Classification of Brain Tumor Diseases Using Data Augmentation and Transfer Learning

Brain Tumor Segmentation Using Enhancement Convolved and Deconvolved CNN Model

Brain Tumor Segmentation Utilizing Thresholding and K-Means Clustering

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