Hydrocephalus classification in brain computed tomography medical images using deep learning

Rub, Salsabeel Abu Al; Alaiad, Ahmad; Hmeidi, Ismail; Quwaider, Muhannad; AlZoubi, Omar

doi:10.1016/j.simpat.2022.102705

Cited by 10 publications

(4 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The research achieved significant accuracy using the DL-embedded ML technique. Al Rub et al [19] use the DL model for the classification of hydrocephalus in brain computed tomography (CT) medical images. They created a precise and non-invasive method for brain hydrocephalus diagnosis.…”

Section: Related Workmentioning

confidence: 99%

Cancerous and Non-Cancerous MRI Classification Using Dual DCNN Approach

Saeed,

Bouhali,

et al. 2024

Bioengineering

View full text Add to dashboard Cite

Brain cancer is a life-threatening disease requiring close attention. Early and accurate diagnosis using non-invasive medical imaging is critical for successful treatment and patient survival. However, manual diagnosis by radiologist experts is time-consuming and has limitations in processing large datasets efficiently. Therefore, efficient systems capable of analyzing vast amounts of medical data for early tumor detection are urgently needed. Deep learning (DL) with deep convolutional neural networks (DCNNs) emerges as a promising tool for understanding diseases like brain cancer through medical imaging modalities, especially MRI, which provides detailed soft tissue contrast for visualizing tumors and organs. DL techniques have become more and more popular in current research on brain tumor detection. Unlike traditional machine learning methods requiring manual feature extraction, DL models are adept at handling complex data like MRIs and excel in classification tasks, making them well-suited for medical image analysis applications. This study presents a novel Dual DCNN model that can accurately classify cancerous and non-cancerous MRI samples. Our Dual DCNN model uses two well-performed DL models, i.e., inceptionV3 and denseNet121. Features are extracted from these models by appending a global max pooling layer. The extracted features are then utilized to train the model with the addition of five fully connected layers and finally accurately classify MRI samples as cancerous or non-cancerous. The fully connected layers are retrained to learn the extracted features for better accuracy. The technique achieves 99%, 99%, 98%, and 99% of accuracy, precision, recall, and f1-scores, respectively. Furthermore, this study compares the Dual DCNN’s performance against various well-known DL models, including DenseNet121, InceptionV3, ResNet architectures, EfficientNetB2, SqueezeNet, VGG16, AlexNet, and LeNet-5, with different learning rates. This study indicates that our proposed approach outperforms these established models in terms of performance.

show abstract

Section: Related Workmentioning

confidence: 99%

Cancerous and Non-Cancerous MRI Classification Using Dual DCNN Approach

Saeed,

Bouhali,

et al. 2024

Bioengineering

View full text Add to dashboard Cite

show abstract

“…The system was proposed using an Adaptative Independent Subspace Analysis (AISA) framework for MRI data analysis, texture features, and feature dimensionality reduction based on t‐SNE embedding for discriminative classification, achieving an accuracy of 94.7% with a kNN classifier. Likewise, Al Rub et al 44 developed a set of models based on deep learning and machine learning to aid in the early diagnosis of congenital hydrocephalus, using a methodology based on image processing (cropping, filtering, normalization, segmentation) and data augmentation (DA), it achieves an accuracy of 98.5%. Thus, although there is a lack of documentation based on the identification and classification of neurological alterations in newborn patients, it is possible to observe that the use of AI techniques and digital image processing are important tools for a medical specialist, due to the high speed with which the information is processed, as well as the high precision that these tools can obtain in the diagnosis and detection of certain alterations, even more so in early ages, where it is vital to detect any situation in order to receive early treatment.…”

Section: Introductionmentioning

confidence: 99%

Intelligent MRI diagnosis of neurological alterations in infants from 4 to 12 months

Calderon‐Uribe,

Morales‐Hernandez,

De Leo‐Jimenez

et al. 2024

Int J Imaging Syst Tech

View full text Add to dashboard Cite

Magnetic resonance imaging is an essential tool for the identification of neurological problems since it provides relevant information on brain development. The aim of the present work was the detection of neurological alterations in newborns from 4 to 12 months of age by segmentation and analysis of lateral ventricles in magnetic resonance images. For this purpose, an automated deep approach based on U‐net is proposed to segment the cerebral ventricles of the newborn. Subsequently, for these regions, features were extracted based on the patient's clinical history and on the shape (area, roundness, normalized central moment, among others) and pixel intensity (mean gray value, contrast level, among others). Once the features were extracted, different types of intelligent models (Logistic Regression, k‐Nearest Neighbors (kNN), and a Convolutional Neural Network) were assessed to detect the presence of neurological alterations. The segmentation phase of the system was tested on 50 patients and the classification phase on 28 patients (11 healthy, 17 with neurological changes). The results show a DICE similarity coefficient of 0.89 and a volume ratio of 1.05 for the segmentation stage and an accuracy of 98%, precision of 100%, sensitivity of 92%, and specificity of 100% for the classification stage using kNN. The last one proved to be the most computationally feasible model, due to the time required for training and inference (0.36 s and 35.2e‐4 s, respectively), as well as the consumption of computational resources (0.1 GB RAM CPU). In conclusion, it is possible to detect neurological alterations in newborns aged 4 to 12 months by segmenting and classifying the lateral ventricles in magnetic resonance images, using image processing techniques, the U‐net, as well as the kNN algorithm. This proposed methodology could play an important role in the early diagnosis and treatment of neurological disorders.

show abstract

“…Glioblastomas, the most malignant and aggressive type of brain tumors, pose significant challenges in diagnosis and treatment. Understanding the intricacies of these diverse brain tumors is crucial for tailoring effective treatment strategies, adding complexity to the understanding of brain pathology 11 .…”

Section: Introductionmentioning

confidence: 99%

Employing deep learning and transfer learning for accurate brain tumor detection

Mathivanan,

Sonaimuthu,

Murugesan

et al. 2024

Sci Rep

View full text Add to dashboard Cite

Artificial intelligence-powered deep learning methods are being used to diagnose brain tumors with high accuracy, owing to their ability to process large amounts of data. Magnetic resonance imaging stands as the gold standard for brain tumor diagnosis using machine vision, surpassing computed tomography, ultrasound, and X-ray imaging in its effectiveness. Despite this, brain tumor diagnosis remains a challenging endeavour due to the intricate structure of the brain. This study delves into the potential of deep transfer learning architectures to elevate the accuracy of brain tumor diagnosis. Transfer learning is a machine learning technique that allows us to repurpose pre-trained models on new tasks. This can be particularly useful for medical imaging tasks, where labelled data is often scarce. Four distinct transfer learning architectures were assessed in this study: ResNet152, VGG19, DenseNet169, and MobileNetv3. The models were trained and validated on a dataset from benchmark database: Kaggle. Five-fold cross validation was adopted for training and testing. To enhance the balance of the dataset and improve the performance of the models, image enhancement techniques were applied to the data for the four categories: pituitary, normal, meningioma, and glioma. MobileNetv3 achieved the highest accuracy of 99.75%, significantly outperforming other existing methods. This demonstrates the potential of deep transfer learning architectures to revolutionize the field of brain tumor diagnosis.

show abstract

Hydrocephalus classification in brain computed tomography medical images using deep learning

Cited by 10 publications

References 29 publications

Cancerous and Non-Cancerous MRI Classification Using Dual DCNN Approach

Cancerous and Non-Cancerous MRI Classification Using Dual DCNN Approach

Intelligent MRI diagnosis of neurological alterations in infants from 4 to 12 months

Employing deep learning and transfer learning for accurate brain tumor detection

Contact Info

Product

Resources

About