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Showing 3 results for Tarokh

Fatemeh Bagheri, Mohammad Jafar Tarokh, Majid Ziaratban,
Volume 8, Issue 2 (7-2020)
Abstract

Background and objective: Automatic semantic segmentation of skin lesions is one of the most important medical requirements in the diagnosis and treatment of skin cancer, and scientists always try to achieve more accurate lesion segmentation systems. Developing an accurate model for lesion segmentation helps in timely diagnosis and appropriate treatment.
Material and Methods: In this study, a two-stage deep learning-based method is presented for accurate segmentation of skin lesions. At the first stage, detection stage, an approximate location of the lesion in a dermoscopy is estimated using deep Yolo v2 network. A sub-image is cropped from the input dermoscopy by considering a margin around the estimated lesion bounding box and then resized to a predetermined normal size. DeepLab convolutional neural network is used at the second stage, segmentation stage, to extract the exact lesion area from the normalized image.
Results: A standard and well-known dataset of dermoscopic images, (ISBI) 2017 dataset, is used to evaluate the proposed method and compare it with the state-of-the-art methods. Our method achieved Jaccard value of 79.05%, which is 2.55% higher than the Jaccard of the winner of the ISIC 2017 challenge.
Conclusion: Experiments demonstrated that the proposed two-stage CNN-based lesion segmentation method outperformed other state-of-the-art methods on the well-known ISIB2017 dataset. High accuracy in detection stage is of most important. Using the detection stage based on Yolov2 before segmentation stage, DeepLab3+ structure with appropriate backbone network, data augmentation, and additional modes of input images are the main reasons of the significant improvement.

Parisa Karimi Darabi, Mohammad Jafar Tarokh,
Volume 8, Issue 3 (10-2020)
Abstract

Background and Objectives: Currently, diabetes is one of the leading causes of death in the world. According to several factors diagnosis of this disease is complex and prone to human error. This study aimed to analyze the risk of having diabetes based on laboratory information, life style and, family history with the help of machine learning algorithms. When the model is trained properly, people can examine their risk of having diabetes.
Material and Methods: To classify patients, by using Python, eight different machine learning algorithms (Logistic Regression, Nearest Neighbor, Decision Tree, Random Forest, Support Vector Machine, Naive Bayesian, Neural Network and Gradient Boosting) were analysed. were evaluated by accuracy, sensitivity, specificity and ROC curve parameters.
ResultsThe model based on the gradient boosting algorithm showed the best performance with a prediction accuracy of %95.50.
ConclusionIn the future, this model can be used for diagnosis diabete. The basis of this study is to do more research and develop models such as other learning machine algorithms.

Kobra Sharifiyan, Mohammad Jafar Tarokh, Seyed Alireza Hashemi Golpayegani,
Volume 10, Issue 4 (12-2022)
Abstract

Background and Objectives: One of the complicated processes in the Ministry of Health is the process of registering pharmaceuticals .Since there are different soft wares for registration of products, and since parallel services concuss waste of time and money, it is necessary to provide integrated software in the form of services. This goal can be achieved when services are identified and combined in integrated soft wares.
Material and Methods: present study is an applied research, In order to identify the services of an integrated system for recording pharmaceutical supplies, first the Gray-Wolf multi-objective optimization (GWO) algorithm was proposed. Then the values of the algorithm parameters were extracted by the goal-based requirements analysis method and the algorithm was implemented. Finally the best services were extracted by the hierarchical analysis process.
Results: Considering that the results of present study are an operational project in the Ministry of Health, by implementing the algorithm proposed by Gray Wolf, services were identified, which can be used to create integrated software for registering pharmaceutical supplies.
Conclusion: Creating an integrated system for registering pharmaceutical supplies is one of the important challenges of the Ministry of Health. This can be achieved by identifying services and combining these services to create an integrated system.


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