Classification of Posterior Fossa CT Brain Slices using Artificial Neural Network

Suberi, Anis Azwani Muhd; Zakaria, Wan Nurshazwani Wan; Tomari, Razali; Fuad, Nik Farhan Nik

doi:10.1016/j.procs.2018.08.163

Cited by 4 publications

(3 citation statements)

References 18 publications

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“…In this classification, posterior fossa slices in CT brain images were sorted for further studies in the diagnosis of stroke. Eleven features were considered for this modeling and this classifier had satisfying accuracy [ 39 ]. In another study, the neural network model was used based on the LVQ algorithm to classify electrocardiogram signals.…”

Section: Discussionmentioning

confidence: 99%

“…Various algorithms and methods have been proposed to classify and construct a model for this purpose. The most common methods in ML are various ANN structures, decision trees (DT), k-nearest neighbors (KNN) and SVM structures which have previously been used in many diagnostic methodologies in medicine [ 7 – 9 , 28 , 29 , 38 , 39 ].…”

Section: Methodsmentioning

confidence: 99%

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A novel diagnostic and prognostic approach for unresponsive patients with anthroponotic cutaneous leishmaniasis using artificial neural networks

et al. 2021

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Cutaneous leishmaniasis (CL) imposes a major health burden throughout the tropical and subtropical regions of the globe. Unresponsive cases are common phenomena occurred upon exposure to the standard drugs. Therefore, rapid detection, prognosis and classification of the disease are crucial for selecting the proper treatment modality. Using machine learning (ML) techniques, this study aimed to detect unresponsive cases of ACL, caused by Leishmania tropica, which will consequently be used for a more effective treatment modality. This study was conducted as a case-control setting. Patients were selected in a major ACL focus from both unresponsive and responsive cases. Nine unique and relevant features of patients with ACL were selected. To categorize the patients, different classifier models such as k-nearest neighbors (KNN), support vector machines (SVM), multilayer perceptron (MLP), learning vector quantization (LVQ) and multipass LVQ were applied and compared for this supervised learning task. Comparison of the receiver operating characteristic graphs (ROC) and confusion plots for the above models represented that MLP was a fairly accurate prediction model to solve this problem. The overall accuracy in terms of sensitivity, specificity and area under ROC curve (AUC) of MLP classifier were 87.8%, 90.3%, 86% and 0.88%, respectively. Moreover, the duration of the skin lesion was the most influential feature in MLP classifier, while gender was the least. The present investigation demonstrated that MLP model could be utilized for rapid detection, accurate prognosis and effective treatment of unresponsive patients with ACL. The results showed that the major feature affecting the responsiveness to treatments is the duration of the lesion. This novel approach is unique and can be beneficial in developing diagnostic, prophylactic and therapeutic measures against the disease. This attempt could be a preliminary step towards the expansion of ML application in future directions.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

A novel diagnostic and prognostic approach for unresponsive patients with anthroponotic cutaneous leishmaniasis using artificial neural networks

et al. 2021

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“…In order to enhance the visibility of the normal and abnormal tissues, the window setting of window width and window center must be in the range of +40 to +80 Hounsefield unit (HU) [32]. It is important to note that both and of 40 HU, recommended by the radiologist provides a solution that satisfies the visibility of acute ischemic in CT image [33].…”

Section: B Pre-processing Dicom Image Conversionmentioning

confidence: 99%

Deep Transfer Learning Application for Automated Ischemic Classification in Posterior Fossa CT Images

Suberi¹,

Nurshazwani²,

Tomari³

et al. 2019

IJACSA

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Computed Tomography (CT) imaging is one of the conventional tools used to diagnose ischemic in Posterior Fossa (PF). Radiologist commonly diagnoses ischemic in PF through CT imaging manually. However, such a procedure could be strenuous and time consuming for large scale images, depending on the expertise and ischemic visibility. With the rapid development of computer technology, automatic image classification based on Machine Learning (ML) is widely been developed as a second opinion to the ischemic diagnosis. The practical performance of ML is challenged by the emergence of deep learning applications in healthcare. In this study, we evaluate the performance of deep transfer learning models of Convolutional Neural Network (CNN); VGG-16, GoogleNet and ResNet-50 to classify the normal and abnormal (ischemic) brain CT images of PF. This is the first study that intensively studies the application of deep transfer learning for automated ischemic classification in the posterior part of brain CT images. The experimental results show that ResNet-50 is capable to achieve the highest accuracy performance in comparison to other proposed models. Overall, this automatic classification provides a convenient and time-saving tool for improving medical diagnosis.

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Hybrid Skull Stripping Method for Brain CT Images

Rahmad

Zakaria

Nazari

et al. 2020

Lecture Notes in Electrical Engineering

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Classification of Posterior Fossa CT Brain Slices using Artificial Neural Network

Cited by 4 publications

References 18 publications

A novel diagnostic and prognostic approach for unresponsive patients with anthroponotic cutaneous leishmaniasis using artificial neural networks

A novel diagnostic and prognostic approach for unresponsive patients with anthroponotic cutaneous leishmaniasis using artificial neural networks

Deep Transfer Learning Application for Automated Ischemic Classification in Posterior Fossa CT Images

Hybrid Skull Stripping Method for Brain CT Images

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