An Intelligent Saliency Segmentation Technique and Classification of Low Contrast Skin Lesion Dermoscopic Images Based on Histogram Decision

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243

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.

Section: Proposed Methodologymentioning

confidence: 99%

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

Rehman

Khan

Mehmood

et al. 2020

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243

116

“…Preprocessing is normally performed in almost all sorts of image analysis and classification to remove the unnecessary detail and enhance the input image quality for better performance (Adeel et al, 2020; Al‐Ameen et al, 2015; Amin, Sharif, Raza, Saba, & Anjum, 2019; Amin, Sharif, Raza, Saba, & Rehman, 2019; Amin, Sharif, Raza, Saba, Sial, & Shad, 2019; Amin, Sharif, Rehman, Raza, & Mufti, 2018; Amin, Sharif, Yasmin, Saba, & Raza, 2019; Ejaz et al, 2018; Ejaz et al, 2020; Iqbal et al, 2019; Iqbal, Ghani, Saba, & Rehman, 2018; Iqbal, Khan, Saba, & Rehman, 2017; Javed, Rahim, & Saba, 2019; Javed, Rahim, Saba, & Rashid, 2019; Javed, Rahim, Saba, & Rehman, 2020; Javed, Saba, Shafry, & Rahim, 2020; Saba, 2020; Saba, Bokhari, et al, 2018). Accordingly, in the proposed approach, image intensities are normalized using histogram equalization (Ali et al, 2016) and Gaussian un‐sharp mask is applied to enhanced its quality.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…In this one‐versus‐rest (1VR) procedure, different N numbers of binary classifiers are combined to classify N number of categories. The main advantage of using EMC‐SVM is to produce accurate and efficient recognition results, even for small data (Javed, Rahim, et al, 2020; Javed, Rahim, & Saba, 2019; Javed, Rahim, Saba, & Rashid, 2019; Javed, Saba, et al, 2020; Khan, Javed, et al, 2019; Khan, Sharif, et al, 2019; Khan, Sharif, Javed, Yasmin, & Saba, 2017; Perveen et al, 2020; Tan, 2006).…”

Section: Proposed Methodologymentioning

confidence: 99%

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

Rehman

2021

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: Introductionmentioning

confidence: 99%

“…As compared to other kinds of cancers worldwide, the ratio of melanomas rapidly increasing, which is an increase of 6% annually. Nowadays, the number has grown to 15 out of 0.1 million and the trend is still increasing (Javed, Rahim, & Saba, 2019; Javed, Rahim, Saba, & Rashid, 2019; Javed, Rahim, Saba, & Rehman, 2020; Javed, Saba, Shafry, & Rahim, 2020). The clinical features of skin cancer‐related pigmented lesions are known as skin cancer ABCD's (asymmetry, border, color, and diameter): Several methods are developed based on image analysis to measure these features.…”

Section: Introductionmentioning

confidence: 99%

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

Saba

2021

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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.