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Brain MR Image Classification for Glioma Tumor detection using Deep Convolutional Neural Network Features

Latif G, Iskandar DNFA, Alghazo J, Butt MM

Curr Med Imaging, 2021;17(1):56-63.
PMID: 32160848 DOI: 10.2174/1573405616666200311122429

BACKGROUND: Detection of brain tumor is a complicated task, which requires specialized skills and interpretation techniques. Accurate brain tumor classification and segmentation from MR images provide an essential choice for medical treatments. Different objects within an MR image have similar size, shape, and density, which makes the tumor classification and segmentation even more complex.
OBJECTIVE: Classification of the brain MR images into tumorous and non-tumorous using deep features and different classifiers to get higher accuracy.
METHODS: In this study, a novel four-step process is proposed; pre-processing for image enhancement and compression, feature extraction using convolutional neural networks (CNN), classification using the multilayer perceptron and finally, tumor segmentation using enhanced fuzzy cmeans method.
RESULTS: The system is tested on 65 cases in four modalities consisting of 40,300 MR Images obtained from the BRATS-2015 dataset. These include images of 26 Low-Grade Glioma (LGG) tumor cases and 39 High-Grade Glioma (HGG) tumor cases. The proposed CNN feature-based classification technique outperforms the existing methods by achieving an average accuracy of 98.77% and a noticeable improvement in the segmentation results are measured.
CONCLUSION: The proposed method for brain MR image classification to detect Glioma Tumor detection can be adopted as it gives better results with high accuracies.
Fulltext ArASL: Arabic Alphabets Sign Language Dataset

Latif G, Mohammad N, Alghazo J, AlKhalaf R, AlKhalaf R

Data Brief, 2019 Apr;23:103777.
PMID: 31372425 DOI: 10.1016/j.dib.2019.103777

A fully-labelled dataset of Arabic Sign Language (ArSL) images is developed for research related to sign language recognition. The dataset will provide researcher the opportunity to investigate and develop automated systems for the deaf and hard of hearing people using machine learning, computer vision and deep learning algorithms. The contribution is a large fully-labelled dataset for Arabic Sign Language (ArSL) which is made publically available and free for all researchers. The dataset which is named ArSL2018 consists of 54,049 images for the 32 Arabic sign language sign and alphabets collected from 40 participants in different age groups. Different dimensions and different variations were present in images which can be cleared using pre-processing techniques to remove noise, center the image, etc. The dataset is made available publicly at https://data.mendeley.com/datasets/y7pckrw6z2/1.
Recent Advancements in Fuzzy C-means Based Techniques for Brain MRI Segmentation

Latif G, Alghazo J, Sibai FN, Iskandar DNFA, Khan AH

Curr Med Imaging, 2021;17(8):917-930.
PMID: 33397241 DOI: 10.2174/1573405616666210104111218

BACKGROUND: Variations of image segmentation techniques, particularly those used for Brain MRI segmentation, vary in complexity from basic standard Fuzzy C-means (FCM) to more complex and enhanced FCM techniques.
OBJECTIVE: In this paper, a comprehensive review is presented on all thirteen variations of FCM segmentation techniques. In the review process, the concentration is on the use of FCM segmentation techniques for brain tumors. Brain tumor segmentation is a vital step in the process of automatically diagnosing brain tumors. Unlike segmentation of other types of images, brain tumor segmentation is a very challenging task due to the variations in brain anatomy. The low contrast of brain images further complicates this process. Early diagnosis of brain tumors is indeed beneficial to patients, doctors, and medical providers.
RESULTS: FCM segmentation works on images obtained from magnetic resonance imaging (MRI) scanners, requiring minor modifications to hospital operations to early diagnose tumors as most, if not all, hospitals rely on MRI machines for brain imaging.
CONCLUSION: In this paper, we critically review and summarize FCM based techniques for brain MRI segmentation.
Recent Advances in Classification of Brain Tumor from MR Images - State of the Art Review from 2017 to 2021

Latif G, Al Falanezi Pmu Edu saAnezi FY, Iskandar DNFA, Bashar A, Alghazo J

Curr Med Imaging, 2022 Jan 17.
PMID: 35040408 DOI: 10.2174/1573405618666220117151726

BACKGROUND: The task of identifying a tumor in the brain is a complex problem that requires sophisticated skills and inference mechanisms to accurately locate the tumor region. The complex nature of the brain tissue makes the problem of locating, segmenting, and ultimately classifying Magnetic Resonance (MR) images a complex problem. The aim of this review paper is to consolidate the details of the most relevant and recent approaches proposed in this domain for the binary and multi-class classification of brain tumors using brain MR images.
OBJECTIVE: In this review paper, a detailed summary of the latest techniques used for brain MR image feature extraction and classification is presented. A lot of research papers have been published recently with various techniques proposed for identifying an efficient method for the correct recognition and diagnosis of brain MR images. The review paper allows researchers in the field to familiarize themselves with the latest developments and be able to propose novel techniques that have not yet been explored in this research domain. In addition, the review paper will facilitate researchers, who are new to machine learning algorithms for brain tumor recognition, to understand the basics of the field and pave the way for them to be able to contribute to this vital field of medical research.
RESULTS: In this paper, the review is performed for all recently proposed methods for both feature extraction and classification. It also identifies the combination of feature extraction methods and classification methods that when combined would be the most efficient technique for the recognition and diagnosis of brain tumor from MR images. In addition, the paper presents the performance metrics particularly the recognition accuracy, of selected research published between 2017- 2021.
Multiclass tumor identification using combined texture and statistical features

Latif G, Bashar A, Awang Iskandar DNF, Mohammad N, Brahim GB, Alghazo JM

Med Biol Eng Comput, 2023 Jan;61(1):45-59.
PMID: 36323980 DOI: 10.1007/s11517-022-02687-w

Early detection and diagnosis of brain tumors are essential for early intervention and eventually successful treatment plans leading to either a full recovery or an increase in the patient lifespan. However, diagnosis of brain tumors is not an easy task since it requires highly skilled professionals, making this procedure both costly and time-consuming. The diagnosis process relying on MR images gets even harder in the presence of similar objects in terms of their density, size, and shape. No matter how skilled professionals are, their task is still prone to human error. The main aim of this work is to propose a system that can automatically classify and diagnose glioma brain tumors into one of the four tumor types: (1) necrosis, (2) edema, (3) enhancing, and (4) non-enhancing. In this paper, we propose a combined texture discrete wavelet transform (DWT) and statistical features based on the first- and second-order features for the accurate classification and diagnosis of multiclass glioma tumors. Four well-known classifiers, namely, support vector machines (SVM), random forest (RF), multilayer perceptron (MLP), and naïve Bayes (NB), are used for classification. The BraTS 2018 dataset is used for the experiments, and with the combined DWT and statistical features, the RF classifier achieved the highest average accuracy whether for separated modalities or combined modalities. The highest average accuracy of 89.59% and 90.28% for HGG and LGG, respectively, was reported in this paper. It has also been observed that the proposed method outperforms similar existing methods reported in the extant literature.