Retinal imaging analysis based on vessel detection

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

“…Brain tumors are also categorized based on their origin: primary brain tumors are the lesions that emerge within the brain whereas secondary brain tumors (metastatic tumors) originate at a different location of the body and move to the brain. There are different categorization schemes designed by researchers but the scheme designed by the World Health Organization (WHO) is considered to be the standard (Aurangzeb, Muhammad Attique, Saba, Javed, & Iqbal, ; Fahad, Khan, Saba, Rehman, & Iqbal, ; Husham, Alkawaz, Saba, Rehman, & Alghamdi, ; Jamal, Hazim Alkawaz, Rehman, & Saba, ; Khan et al, , ; Rehman, Abbas, Saba, Rahman, et al, ; Rehman, Abbas, Saba, Mehmood, et al, ; Rehman, Abbas, Saba, Mahmood, & Kolivand, ; Saba, ; Saba, Al‐Zahrani, & Rehman, ; Saba, Rehman, Mehmood, Kolivand, Uddin, et al, ; Saba, Rehman, Mehmood, Kolivand, & Sharif, ). WHO has made more than 120 categories for a brain tumor and each category is further graded from I to IV (Louis et al, ).…”

Section: Introductionmentioning

confidence: 99%

Deep learning model integrating features and novel classifiers fusion for brain tumor segmentation

Iqbal

Saba

et al. 2019

Self Cite

Automatic and precise segmentation and classification of tumor area in medical images is still a challenging task in medical research. Most of the conventional neural network based models usefully connected or convolutional neural networks to perform segmentation and classification. In this research, we present deep learning models using long short term memory (LSTM) and convolutional neural networks (ConvNet) for accurate brain tumor delineation from benchmark medical images. The two different models, that is, ConvNet and LSTM networks are trained using the same data set and combined to form an ensemble to improve the results. We used publicly available MICCAI BRATS 2015 brain cancer data set consisting of MRI images of four modalities T1, T2, T1c, and FLAIR. To enhance the quality of input images, multiple combinations of preprocessing methods such as noise removal, histogram equalization, and edge enhancement are formulated and best performer combination is applied. To cope with the class imbalance problem, class weighting is used in proposed models. The trained models are tested on validation data set taken from the same image set and results obtained from each model are reported. The individual score (accuracy) of ConvNet is found 75% whereas for LSTM based network produced 80% and ensemble fusion produced 82.29% accuracy.

“…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, ). The mobile computing approaches mainly focused on the reduction of computational complexity with respect to system performance by offloading various tasks to the cloud server from mobile devices (Abbas et al, ; Iftikhar, Fatima, Rehman, Almazyad, & Saba, ; Saba, Bokhari, Sharif, Yasmin, & Raza, ; Wang, Chen, & Wang, ).…”

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.