Microscopic brain tumor detection and classification using <scp>3D CNN</scp> and feature selection architecture

Rehman, Amjad; Khan, Muhammad Attique; Mehmood, Zahid; Tariq, Usman; Noor, Ayesha

doi:10.1002/jemt.23597

Cited by 208 publications

(118 citation statements)

References 58 publications

(77 reference statements)

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“…The BRATS2018 dataset was used for the experimental process and an accuracy of 93.85% is reported. Rehman et al [12] presented a method for the automated detection of brain tumors using a deep learning. The study is useful for the microscopic detection of the tumor.…”

Section: Related Workmentioning

confidence: 99%

“…However, in either case, diagnosis is crucial and it needs expert radiologists [11]. The more recent imaging technology shows a huge success in the area of medical imaging for the diagnosis and detection of dangerous human diseases such as brain tumors [12], skin cancer [13], stomach cancer [14,15], lung cancer [16], blood cancer [17], and name a few more [18][19][20][21]. For brain tumors, MRI (Magnetic Resonance Imaging) and CT (Computed Tomography) scans are more useful imaging technologies [22].…”

Section: Introductionmentioning

confidence: 99%

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A decision support system for multimodal brain tumor classification using deep learning

Sharif

Khan

Alhussein

et al. 2021

Complex Intell. Syst.

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114

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Multiclass classification of brain tumors is an important area of research in the field of medical imaging. Since accuracy is crucial in the classification, a number of techniques are introduced by computer vision researchers; however, they still face the issue of low accuracy. In this article, a new automated deep learning method is proposed for the classification of multiclass brain tumors. To realize the proposed method, the Densenet201 Pre-Trained Deep Learning Model is fine-tuned and later trained using a deep transfer of imbalanced data learning. The features of the trained model are extracted from the average pool layer, which represents the very deep information of each type of tumor. However, the characteristics of this layer are not sufficient for a precise classification; therefore, two techniques for the selection of features are proposed. The first technique is Entropy–Kurtosis-based High Feature Values (EKbHFV) and the second technique is a modified genetic algorithm (MGA) based on metaheuristics. The selected features of the GA are further refined by the proposed new threshold function. Finally, both EKbHFV and MGA-based features are fused using a non-redundant serial-based approach and classified using a multiclass SVM cubic classifier. For the experimental process, two datasets, including BRATS2018 and BRATS2019, are used without increase and have achieved an accuracy of more than 95%. The precise comparison of the proposed method with other neural nets shows the significance of this work.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A decision support system for multimodal brain tumor classification using deep learning

Sharif

Khan

Alhussein

et al. 2021

Complex Intell. Syst.

Self Cite

114

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“…There are two main categories of features: handcrafted and non‐handcrafted features. The first category employed with traditional machine learning techniques while the second works for deep learning (Rehman, Khan, Mehmood, et al, 2020; Rehman, Khan, Saba, et al, 2020; Saba, 2019; Saba, Mohamed, El‐Affendi, Amin, & Sharif, 2020).…”

Section: Handcrafted Features For Traditional Machine Learning‐based Classificationmentioning

confidence: 99%

Computer vision for microscopic skin cancer diagnosis using handcrafted and non‐handcrafted features

Saba

2021

Microscopy Res & Technique

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Skin covers the entire body and is the largest organ. Skin cancer is one of the most dreadful cancers that is primarily triggered by sensitivity to ultraviolet rays from the sun. However, the riskiest is melanoma, although it starts in a few different ways.The patient is extremely unaware of recognizing skin malignant growth at the initial stage. Literature is evident that various handcrafted and automatic deep learning features are employed to diagnose skin cancer using the traditional machine and deep learning techniques. The current research presents a comparison of skin cancer diagnosis techniques using handcrafted and non-handcrafted features. Additionally, clinical features such as Menzies method, seven-point detection, asymmetry, border color and diameter, visual textures (GRC), local binary patterns, Gabor filters, random fields of Markov, fractal dimension, and an oriental histography are also explored in the process of skin cancer detection. Several parameters, such as jacquard index, accuracy, dice efficiency, preciseness, sensitivity, and specificity, are compared on benchmark data sets to assess reported techniques. Finally, publicly available skin cancer data sets are described and the remaining issues are highlighted.

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“…The outcome of this paper suggests that a radiologist can classify brain tumors more precisely with the proposed method. A brain detection approach using 3D-CNN was proposed in [113], where the model was tested on BRATS 2015, 2017, and 2018 challenge datasets. From the experimental results, it was clear that the proposed model showed the highest classification accuracy on BRATS 2015 and a comparable accuracy with the existing methods.…”

Section: ) Dl-based Approaches In Brain Tumor Diagnosismentioning

confidence: 99%

Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

et al. 2021

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Brain is the controlling center of our body. With the advent of time, newer and newer brain diseases are being discovered. Thus, because of the variability of brain diseases, existing diagnosis or detection systems are becoming challenging and are still an open problem for research. Detection of brain diseases at an early stage can make a huge difference in attempting to cure it. In recent years, the use of artificial intelligence (AI) is surging through all spheres of science, and no doubt, it is revolutionizing the field of neurology. Application of AI in medical science has made brain disease prediction and detection more accurate and precise. In this study, we present a review on recent machine learning and deep learning approaches in detecting four brain diseases such as Alzheimer's disease (AD), brain tumor, epilepsy, and Parkinson's disease. 147 recent articles on four brain diseases are reviewed considering diverse machine learning and deep learning approaches, modalities, datasets etc. Twenty-two datasets are discussed which are used most frequently in the reviewed articles as a primary source of brain disease data. Moreover, a brief overview of different feature extraction techniques that are used in diagnosing brain diseases is provided. Finally, key findings from the reviewed articles are summarized and a number of major issues related to machine learning/deep learning-based brain disease diagnostic approaches are discussed. Through this study, we aim at finding the most accurate technique for detecting different brain diseases which can be employed for future betterment. INDEX TERMS Alzheimer's disease, brain tumor, deep learning, epilepsy, Parkinson's disease, machine learning NOMENCLATURE The most commonly used abbreviations in this survey are summarized in Table 1.

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Microscopic brain tumor detection and classification using 3D CNN and feature selection architecture

Cited by 208 publications

References 58 publications

A decision support system for multimodal brain tumor classification using deep learning

A decision support system for multimodal brain tumor classification using deep learning

Computer vision for microscopic skin cancer diagnosis using handcrafted and non‐handcrafted features

Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

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