A New Approach for Brain Tumor Segmentation and Classification Based on Score Level Fusion Using Transfer Learning

Amin, Javeria; Sharif, Muhammad; Yasmin, Mussarat; Saba, Tanzila; Anjum, Muhammad Almas; Fernandes, Steven Lawrence

doi:10.1007/s10916-019-1453-8

Cited by 122 publications

(68 citation statements)

References 43 publications

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“…In medical image analysis, various methods are introduced to diagnose and cure chronic diseases (Abbas et al, ; Abbas, Saba, Rehman, Mehmood, Javaid, et al ; Abbas, Saba, Rehman, Mehmood, Kolivand, et al ; Amin et al, ; Amin, Sharif, Yasmin, Saba, & Raza, ; Jamal, Hazim Alkawaz, Rehman, & Saba, ; Mughal, Muhammad, Sharif, Saba, & Rehman, ; Mughal, Sharif, Muhammad, & Saba, ; Mughal, Muhammad, Sharif, Rehman, & Saba, ; Ullah et al, ; Saba, 2020). Accordingly, several in‐depth techniques/reviews are presented (Javed, Rahim, Saba, & Rehman, ; Norouzi et al, ; Saba, Bokhari, Sharif, Yasmin, & Raza, ; Saba, Rehman, Mehmood, Kolivand, & Sharif, ), and automated systems are developed in state of art (Saba et al, , Saba, Al‐Zahrani, & Rehman, ; Rehman, Abbas, Saba, Mahmood, & Kolivand, ; Rehman, Abbas, Saba, Mehmood, et al, ; Rehman, Abbas, Saba, Rahman, et al, ; Rahim, Norouzi, Rehman, & Saba, ; Rahim, Rehman, Kurniawan, & Saba, ; Iftikhar, Fatima, Rehman, Almazyad, & Saba, ; Fahad et al, ).…”

Section: Related Workmentioning

confidence: 99%

“…Accordingly, several in‐depth techniques/reviews are presented (Javed, Rahim, Saba, & Rehman, ; Norouzi et al, ; Saba, Bokhari, Sharif, Yasmin, & Raza, ; Saba, Rehman, Mehmood, Kolivand, & Sharif, ), and automated systems are developed in state of art (Saba et al, , Saba, Al‐Zahrani, & Rehman, ; Rehman, Abbas, Saba, Mahmood, & Kolivand, ; Rehman, Abbas, Saba, Mehmood, et al, ; Rehman, Abbas, Saba, Rahman, et al, ; Rahim, Norouzi, Rehman, & Saba, ; Rahim, Rehman, Kurniawan, & Saba, ; Iftikhar, Fatima, Rehman, Almazyad, & Saba, ; Fahad et al, ). However, among all, detection of lesions from dermoscopy images is a challenging task from the last few years, and various techniques are developed by several researchers using computer vision (CV) and machine learning (Amin, Sharif, Yasmin, Saba, & Raza, ; Amin et al, ; Husham, Alkawaz, Saba, Rehman, & Alghamdi, ; Iqbal et al, ; Iqbal, Ghani, Saba, & Rehman, ; Iqbal, Khan, Saba, & Rehman, ; Jamal et al, ; Fahad et al, ). Pathan, Prabhu, and Siddalingaswamy () developed a novel hair detection and segmentation method by utilizing specific properties of the lesion.…”

Section: Related Workmentioning

confidence: 99%

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Microscopic melanoma detection and classification: A framework of pixel‐based fusion and multilevel features reduction

Rehman

Khan

Mehmood

et al. 2020

Microscopy Res & Technique

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The numbers of diagnosed patients by melanoma are drastic and contribute more deaths annually among young peoples. An approximately 192,310 new cases of skin cancer are diagnosed in 2019, which shows the importance of automated systems for the diagnosis process. Accordingly, this article presents an automated method for skin lesions detection and recognition using pixel‐based seed segmented images fusion and multilevel features reduction. The proposed method involves four key steps: (a) mean‐based function is implemented and fed input to top‐hat and bottom‐hat filters which later fused for contrast stretching, (b) seed region growing and graph‐cut method‐based lesion segmentation and fused both segmented lesions through pixel‐based fusion, (c) multilevel features such as histogram oriented gradient (HOG), speeded up robust features (SURF), and color are extracted and simple concatenation is performed, and (d) finally variance precise entropy‐based features reduction and classification through SVM via cubic kernel function. Two different experiments are performed for the evaluation of this method. The segmentation performance is evaluated on PH2, ISBI2016, and ISIC2017 with an accuracy of 95.86, 94.79, and 94.92%, respectively. The classification performance is evaluated on PH2 and ISBI2016 dataset with an accuracy of 98.20 and 95.42%, respectively. The results of the proposed automated systems are outstanding as compared to the current techniques reported in state of art, which demonstrate the validity of the proposed method.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Microscopic melanoma detection and classification: A framework of pixel‐based fusion and multilevel features reduction

Rehman

Khan

Mehmood

et al. 2020

Microscopy Res & Technique

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“…In literature, the maximum techniques for blood cell localization [22,23], segmentation and classification are based on thresholding [24][25][26][27][28][29][30][31][32][33][34][35][36]. Thresholding methods cannot provide precise results if cells on the blood smear image are occluded with other cells.…”

Section: Related Workmentioning

confidence: 99%

Recognition of Different Types of Leukocytes Using YOLOv2 and Optimized Bag-of-Features

et al. 2020

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White blood cells (WBCs) protect human body against different types of infections including fungal, parasitic, viral, and bacterial. The detection of abnormal regions in WBCs is a difficult task. Therefore a method is proposed for the localization of WBCs based on YOLOv2-Nucleus-Cytoplasm, which contains darkNet-19 as a basenetwork of the YOLOv2 model. In this model features are extracted from LeakyReLU-18 of darkNet-19 and supplied as an input to the YOLOv2 model. The YOLOv2-Nucleus-Cytoplasm model localizes and classifies the WBCs with maximum score labels. It also localize the WBCs into the blast and non-blast cells. After localization, the bag-of-features are extracted and optimized by using particle swarm optimization(PSO). The improved feature vector is fed to classifiers i.e., optimized naïve Bayes (O-NB) & optimized discriminant analysis (O-DA) for WBCs classification. The experiments are performed on LISC, ALL-IDB1, and ALL-IDB2 datasets.

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“…The existing DFU methods are discussed in detail [7][8][9][10][11][12][13]. The edge detection, morphological operations, and unsupervised clustering methodologies are utilized for the segmentation of DFU [14].…”

Section: Related Workmentioning

confidence: 99%

An Integrated Design for Classification and Localization of Diabetic Foot Ulcer Based on CNN and YOLOv2-DFU Models

et al. 2020

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A New Approach for Brain Tumor Segmentation and Classification Based on Score Level Fusion Using Transfer Learning

Cited by 122 publications

References 43 publications

Microscopic melanoma detection and classification: A framework of pixel‐based fusion and multilevel features reduction

Microscopic melanoma detection and classification: A framework of pixel‐based fusion and multilevel features reduction

Recognition of Different Types of Leukocytes Using YOLOv2 and Optimized Bag-of-Features

An Integrated Design for Classification and Localization of Diabetic Foot Ulcer Based on CNN and YOLOv2-DFU Models

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