Hybrid Convolutional Neural Network-Based Diagnosis System for Intracranial Hemorrhage

Barin, Sezin; Saribaş, Murat; Çiltaş, Beyza Gülizar; Güraksın, Gür Emre; Köse, Utku

doi:10.18662/brain/12.4/236

Cited by 6 publications

(3 citation statements)

References 8 publications

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“…Barin et al [ 16 ] proposed a hybrid CNN model by combining EfficientNet-B3 and Inception-ResNet-V2 for ICH diagnosis. However, the proposed work is limited to the overfitting problem, despite reaching 98% accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

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Automated Computer-Aided Detection and Classification of Intracranial Hemorrhage Using Ensemble Deep Learning Techniques

Umapathy,

Murugappan,

Bharathi

et al. 2023

Diagnostics

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Diagnosing Intracranial Hemorrhage (ICH) at an early stage is difficult since it affects the blood vessels in the brain, often resulting in death. We propose an ensemble of Convolutional Neural Networks (CNNs) combining Squeeze and Excitation–based Residual Networks with the next dimension (SE-ResNeXT) and Long Short-Term Memory (LSTM) Networks in order to address this issue. This research work primarily used data from the Radiological Society of North America (RSNA) brain CT hemorrhage challenge dataset and the CQ500 dataset. Preprocessing and data augmentation are performed using the windowing technique in the proposed work. The ICH is then classified using ensembled CNN techniques after being preprocessed, followed by feature extraction in an automatic manner. ICH is classified into the following five types: epidural, intraventricular, subarachnoid, intra-parenchymal, and subdural. A gradient-weighted Class Activation Mapping method (Grad-CAM) is used for identifying the region of interest in an ICH image. A number of performance measures are used to compare the experimental results with various state-of-the-art algorithms. By achieving 99.79% accuracy with an F-score of 0.97, the proposed model proved its efficacy in detecting ICH compared to other deep learning models. The proposed ensembled model can classify epidural, intraventricular, subarachnoid, intra-parenchymal, and subdural hemorrhages with an accuracy of 99.89%, 99.65%, 98%, 99.75%, and 99.88%. Simulation results indicate that the suggested approach can categorize a variety of intracranial bleeding types. By implementing the ensemble deep learning technique using the SE-ResNeXT and LSTM models, we achieved significant classification accuracy and AUC scores.

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Section: Literature Reviewmentioning

confidence: 99%

“…The feed-forward deep learning method achieved an optimal precision value of 96.43%. The Inception ResNet-v2, EfficientNet-B3, and hybrid models were used in [ 16 ] to classify different types of ICH. Using the RSNA dataset, the authors trained and tested their network models.…”

Section: Literature Reviewmentioning

confidence: 99%

Automated Computer-Aided Detection and Classification of Intracranial Hemorrhage Using Ensemble Deep Learning Techniques

Umapathy,

Murugappan,

Bharathi

et al. 2023

Diagnostics

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“…This design dramatically expedited the training of such deep neural networks[24]. Figure4shows the basic architecture of the InceptionResNetV2[25].…”

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confidence: 99%

Web-based CNN Application for Arabica Coffee Leaf Disease Prediction in Smart Agriculture

Aufar¹,

Abdillah²,

Romadoni³

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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In the agriculture industry, plant diseases provide difficulty, particularly for Arabica coffee production. A first step in eliminating and treating infections to avoid crop damage is recognizing ailments on Arabica coffee leaves. Convolutional neural networks (CNN) are rapidly advancing, making it possible to diagnose Arabica coffee leaf damage without a specialist's help. CNN is aimed to find features adaptively through backpropagation by adding layers including convolutional layers and pooling layers. This study aims to optimize and increase the accuracy of Arabica coffee leaf disease classification utilizing the neural network architectures: ResNet50, InceptionResNetV4, MobileNetV2, and DensNet169. Additionally, this research presents an interactive web platform integrated with the Arabica coffee leaf disease prediction system. Inside this research, 5000 image data points will be divided into five classes—Phoma, Rust, Cescospora, healthy, and Miner—to assess the efficacy of CNN architecture in classifying images of Arabica coffee leaf disease. 80:10:10 is the ratio between training data, validation, and testing. In the testing findings, the InceptionResnetV2 and DensNet169 designs had the highest accuracy, at 100%, followed by the MobileNetV2 architecture at 99% and the ResNet50 architecture at 59%. Even though MobileNetV2 is not more accurate than InceptionResnetV2 and DensNet169, MobileNetV2 is the smallest of the three models. The MobileNetV2 paradigm was chosen for web application development. The system accurately identified and advised treatment for Arabica coffee leaf diseases, as shown by the system's implementation outcomes.

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Classification of brain hemorrhage computed tomography images using OzNet hybrid algorithm

Özaltın

Coskun²,

Yeniay

et al. 2022

Int J Imaging Syst Tech

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Classification of brain hemorrhage computed tomography (CT) images provides a better diagnostic implementation for emergency patients. Attentively, each brain CT image must be examined by doctors. This situation is time-consuming, exhausting, and sometimes leads to making errors. Hence, we aim to find the best algorithm owing to a requirement for automatic classification of CT images to detect brain hemorrhage. In this study, we developed OzNet hybrid algorithm, which is a novel convolution neural networks (CNN) algorithm. Although OzNet achieves high classification performance, we combine it with Neighborhood Component Analysis (NCA) and many classifiers: Artificial neural networks (ANN), Adaboost, Bagging, Decision Tree, K-Nearest Neighbor (K-NN), Linear Discriminant Analysis (LDA), Naïve Bayes and Support Vector Machines (SVM). In addition, Oznet is utilized for feature extraction, where 4096 features are extracted from the fully connected layer. These features are reduced to have significant and informative features with minimum loss by NCA. Eventually, we use these classifiers to classify these significant features. Finally, experimental results display that OzNet-NCA-ANN excellent classifier model and achieves 100% accuracy with created Dataset 2 from Brain Hemorrhage CT images.

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Hybrid Convolutional Neural Network-Based Diagnosis System for Intracranial Hemorrhage

Cited by 6 publications

References 8 publications

Automated Computer-Aided Detection and Classification of Intracranial Hemorrhage Using Ensemble Deep Learning Techniques

Automated Computer-Aided Detection and Classification of Intracranial Hemorrhage Using Ensemble Deep Learning Techniques

Web-based CNN Application for Arabica Coffee Leaf Disease Prediction in Smart Agriculture

Classification of brain hemorrhage computed tomography images using OzNet hybrid algorithm

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