Computer-assisted brain tumor type discrimination using magnetic resonance imaging features

Akram

Sharif

et al. 2019

Self Cite

Skin cancer is being a most deadly type of cancers which have grown extensively worldwide from the last decade. For an accurate detection and classification of melanoma, several measures should be considered which include, contrast stretching, irregularity measurement, selection of most optimal features, and so forth. A poor contrast of lesion affects the segmentation accuracy and also increases classification error. To overcome this problem, an efficient model for accurate border detection and classification is presented. The proposed model improves the segmentation accuracy in its preprocessing phase, utilizing contrast enhancement of lesion area compared to the background. The enhanced 2D blue channel is selected for the construction of saliency map, at the end of which threshold function produces the binary image. In addition, particle swarm optimization (PSO) based segmentation is also utilized for accurate border detection and refinement. Few selected features including shape, texture, local, and global are also extracted which are later selected based on genetic algorithm with an advantage of identifying the fittest chromosome. Finally, optimized features are later fed into the support vector machine (SVM) for classification. Comprehensive experiments have been carried out on three datasets named as PH2, ISBI2016, and ISIC (i.e., ISIC MSK‐1, ISIC MSK‐2, and ISIC UDA). The improved accuracy of 97.9, 99.1, 98.4, and 93.8%, respectively obtained for each dataset. The SVM outperforms on the selected dataset in terms of sensitivity, precision rate, accuracy, and FNR. Furthermore, the selection method outperforms and successfully removed the redundant features.

Section: Related Workmentioning

confidence: 99%

Construction of saliency map and hybrid set of features for efficient segmentation and classification of skin lesion

Akram

Sharif

et al. 2019

Self Cite

“…During the last few decades, the use of Information and Communication Technologies (ICT) has grown exponentially and it has positive impacts on our daily lives (Husham, Alkawaz, Saba, Rehman, & Alghamdi, ; Rad, Rahim, Rehman, & Saba, ; Rehman & Saba, ; Saba, ). Among other applications of ICT, one of the valuable applications could be observed in the field of E‐health care industry, which is based on the concept of providing health care services to patients at any geographical location around the clock (Fahad, Ghani Khan, Saba, Rehman, & Iqbal, ; Iqbal, Ghani, Saba, & Rehman, ; Iqbal, Khan, Saba, & Rehman, ). The mobile E‐health systems are highly flexible to facilitate the expert for transmission of the health care data remotely with minimum cost (Jamal, Hazim Alkawaz, Rehman, & Saba, ; Rahim, Norouzi, Rehman, & Saba, ; Rahim, Rehman, Kurniawan, & Saba, ; Saba, Al‐Zahrani, & Rehman, ; Waheed, Alkawaz, Rehman, Almazyad, & Saba, ).…”

Section: Introductionmentioning

confidence: 99%

Cloud‐based decision support system for the detection and classification of malignant cells in breast cancer using breast cytology images

Saba

Islam

et al. 2019

Self Cite

The advancement of computer‐ and internet‐based technologies has transformed the nature of services in healthcare by using mobile devices in conjunction with cloud computing. The classical phenomenon of patient–doctor diagnostics is extended to a more robust advanced concept of E‐health, where remote online/offline treatment and diagnostics can be performed. In this article, we propose a framework which incorporates a cloud‐based decision support system for the detection and classification of malignant cells in breast cancer, while using breast cytology images. In the proposed approach, shape‐based features are used for the detection of tumor cells. Furthermore, these features are used for the classification of cells into malignant and benign categories using Naive Bayesian and Artificial Neural Network. Moreover, an important phase addressed in the proposed framework is the grading of the affected cells, which could help in grade level necessary medical procedures for patients during the diagnostic process. For demonstrating the e effectiveness of the proposed approach, experiments are performed on real data sets comprising of patients data, which has been collected from the pathology department of Lady Reading Hospital of Pakistan. Moreover, a cross‐validation technique has been performed for the evaluation of the classification accuracy, which shows performance accuracy of 98% as compared to physical methods used by a pathologist for the detection and classification of the malignant cell. Experimental results show that the proposed approach has significantly improved the detection and classification of the malignant cells in breast cytology images.

“…Different types of tumors are classified into different categories by researchers (Rehman, Abbas, Saba, Mahmood, & Kolivand, ; Rehman et al, ; Saba, Rehman, Mehmood, Kolivand, & Sharif, ). Various researchers have used different characteristics of tumors to classify them (Fahad, Ghani Khan, Saba, Rehman, & Iqbal, ; Iqbal, Ghani, Saba, & Rehman, ; Iqbal, Khan, Saba, & Rehman, ; Mughal, Muhammad, Sharif, Rehman, & Saba, ; Mughal, Sharif, Muhammad, & Saba, ; Rehman, Abbas, Saba, Mahmood, & Kolivand, ; Rehman, Abbas, Saba, Mehmood, et al, ; Rehman et al, ; Sadad, Munir, Saba, & Hussain, ). WHO categorization is considered to be the standard in the world.…”

Section: Introductionmentioning

confidence: 99%

Feature enhancement framework for brain tumor segmentation and classification

Tahir

Iqbal

et al. 2019

Self Cite

Automatic medical image analysis is one of the key tasks being used by the medical community for disease diagnosis and treatment planning. Statistical methods are the major algorithms used and consist of few steps including preprocessing, feature extraction, segmentation, and classification. Performance of such statistical methods is an important factor for their successful adaptation. The results of these algorithms depend on the quality of images fed to the processing pipeline: better the images, higher the results. Preprocessing is the pipeline phase that attempts to improve the quality of images before applying the chosen statistical method. In this work, popular preprocessing techniques are investigated from different perspectives where these preprocessing techniques are grouped into three main categories: noise removal, contrast enhancement, and edge detection. All possible combinations of these techniques are formed and applied on different image sets which are then passed to a predefined pipeline of feature extraction, segmentation, and classification. Classification results are calculated using three different measures: accuracy, sensitivity, and specificity while segmentation results are calculated using dice similarity score. Statistics of five high scoring combinations are reported for each data set. Experimental results show that application of proper preprocessing techniques could improve the classification and segmentation results to a greater extent. However, the combinations of these techniques depend on the characteristics and type of data set used.