A comparative study of features selection for skin lesion detection from dermoscopic images

2021

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Similar to other biometric systems such as fingerprint, face, DNA, iris classification could assist law enforcement agencies in identifying humans. Iris classification technology helps law‐enforcement agencies to recognize humans by matching their iris with iris data sets. However, iris classification is challenging in the real environment due to its invertible and complex texture variations in the human iris. Accordingly, this article presents an improved Oriented FAST and Rotated BRIEF with Bag‐of‐Words model to extract distinct and robust features from the iris image, followed by ensemble multi‐class‐SVM to classify iris. The proposed methodology consists of four main steps; first, iris image normalization and enhancement; second, localizing iris region; third, iris feature extraction; finally, iris classification using ensemble multi‐class support vector machine. For preprocessing of input images, histogram equalization, Gaussian mask and median filters are applied. The proposed technique is tested on two benchmark databases, that is, CASIA‐v1 and iris image database, and achieved higher accuracy than other existing techniques reported in state of the art.

Section: Proposed Methodologymentioning

confidence: 99%

Light microscopic iris classification using ensemble multi‐class support vector machine

2021

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“…The deep learning is the emerging technology in machine learning and has attracted significant attention in medical image processing especially for brain tumor detection (Iqbal, Ghani, Saba, & Rehman, 2018; Khan et al, 2020). Additionally, in the area of medical image processing, machine learning has attracted high attention and comes out with notable performance for tumor detection for various modalities such as dermoscopy (Afza, Khan, Sharif, & Rehman, 2019; Javed, Rahim, Saba, & Rehman, 2020), MRI (Nazir, Khan, Saba, & Rehman, 2019;), Mammography (Marie‐Sainte, Saba, Alsaleh, Alotaibi, & Bin, 2019; Saba, Khan, Islam, et al, 2019), and so on.…”

Section: Related Workmentioning

confidence: 99%

“…(Iqbal, Ghani, Saba, & Rehman, 2018;Khan et al, 2020). Additionally, in the area of medical image processing, machine learning has attracted high attention and comes out with notable performance for tumor detection for various modalities such as dermoscopy (Afza, Khan, Sharif, & Rehman, 2019;Javed, Rahim, Saba, & Rehman, 2020), MRI (Nazir, Khan, Saba, & Rehman, 2019;), Mammography (Marie-Sainte, Saba, Alsaleh, Alotaibi, & Bin, 2019;Saba, Khan, Islam, et al, 2019), and so on. Saba, Mohamed, et al (2020) 2019introduced a deep learning model for different brain modalities segmentation.…”

Section: Introductionmentioning

confidence: 99%

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

Khan

Mehmood

et al. 2020

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232

116

Brain tumor is one of the most dreadful natures of cancer and caused a huge number of deaths among kids and adults from the past few years. According to WHO standard, the 700,000 humans are being with a brain tumor and around 86,000 are diagnosed since 2019. While the total number of deaths due to brain tumors is 16,830 since 2019 and the average survival rate is 35%. Therefore, automated techniques are needed to grade brain tumors precisely from MRI scans. In this work, a new deep learning-based method is proposed for microscopic brain tumor detection and tumor type classification. A 3D convolutional neural network (CNN) architecture is designed at the first step to extract brain tumor and extracted tumors are passed to a pretrained CNN model for feature extraction. The extracted features are transferred to the correlation-based selection method and as the output, the best features are selected. These selected features are validated through feed-forward neural network for final classification. Three BraTS datasets 2015, 2017, and 2018 are utilized for experiments, validation, and accomplished an accuracy of 98.32, 96.97, and 92.67%, respectively. A comparison with existing techniques shows the proposed design yields comparable accuracy.

“…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, ). 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, ).…”

Section: Related Workmentioning

confidence: 99%

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

Khan

Mehmood

et al. 2020

Self Cite

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.