Digital Dental X-ray Database for Caries Screening

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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.

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

Khan

Islam

et al. 2019

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“…The main contribution achieved in this article could be summarized as: Salient shape and texture based features are extracted from optic fundus images based on accurate segmentation techniques to detect EXs. Optimization of features is performed through the use of evolutionary Genetic Algorithm (GA), which results in the optimal selection of best features from existing features. The optimization result is an upgradation in terms of computational performance (Rad, Rahim, Rehman, & Saba, ). Three well‐known classifiers [Naïve Bayesian (NB), SVM, and Artificial Neural Network (ANN)] are trained to classify EXs in optic fundus images. Similarly, an ensemble based classifier is used to select the best classifier for EXs classification in fundus images through majority voting scheme (Iqbal, Khan, Saba, & Rehman, ; Iqbal, Ghani, Saba, & Rehman, ). …”

Section: Introduction and Related Workmentioning

confidence: 99%

“…• Optimization of features is performed through the use of evolutionary Genetic Algorithm (GA), which results in the optimal selection of best features from existing features. The optimization result is an upgradation in terms of computational performance (Rad, Rahim, Rehman, & Saba, 2016).…”

mentioning

confidence: 99%

An ensemble classification of exudates in color fundus images using an evolutionary algorithm based optimal features selection

Ullah

Saba

Islam

et al. 2019

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Atomic recognition of the Exudates (EXs), the major symbol of diabetic retinopathy is essential for automated retinal images analysis. In this article, we proposed a novel machine learning technique for early detection and classification of EXs in color fundus images. The major challenge observed in the classification technique is the selection of optimal features to reduce computational time and space complexity and to provide a high degree of classification accuracy. To address these challenges, this article proposed an evolutionary algorithm based solution for optimal feature selection, which accelerates the classification process and reduces computational complexity. Similarly, three well‐known classifiers that is, Naïve Bayes classifier, Support Vector Machine, and Artificial Neural Network are used for the classification of EXs. Moreover, an ensemble‐based classifier is used for the selection of best classifier on the basis of majority voting technique. Experiments are performed on three well‐known benchmark datasets and a real dataset developed at local Hospital. It has been observed that the proposed technique achieved an accuracy of 98% in the detection and classification of EXs in color fundus images.

“…With the help of the advancement of machine vision (MV) technology, prevention from severe consequences as mentioned above is possible through various digital image‐processing techniques. These techniques extract the information of the retina and analyze the retinal vessels to help in decision‐making process by ophthalmologist at later stage(Abràmoff, Garvin, & Sonka, ; Marín, Aquino, Gegúndez‐Arias, & Bravo, ; Mundher, Muhamad, Rehman, Saba, & Kausar, ; Norouzi et al, ; Rad, Rahim, Rehman, & Saba, ).…”

Section: Introductionmentioning

confidence: 99%

Retinal imaging analysis based on vessel detection

Jamal

Alkawaz

Rehman

et al. 2017

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With an increase in the advancement of digital imaging and computing power, computationally intelligent technologies are in high demand to be used in ophthalmology cure and treatment. In current research, Retina Image Analysis (RIA) is developed for optometrist at Eye Care Center in Management and Science University. This research aims to analyze the retina through vessel detection. The RIA assists in the analysis of the retinal images and specialists are served with various options like saving, processing and analyzing retinal images through its advanced interface layout. Additionally, RIA assists in the selection process of vessel segment; processing these vessels by calculating its diameter, standard deviation, length, and displaying detected vessel on the retina. The Agile Unified Process is adopted as the methodology in developing this research. To conclude, Retina Image Analysis might help the optometrist to get better understanding in analyzing the patient's retina. Finally, the Retina Image Analysis procedure is developed using MATLAB (R2011b). Promising results are attained that are comparable in the state of art.