Biomedical Image Processing / Medical Image Processing
Kambiz Rahbar; Fatemeh Taheri
Volume 17, Issue 2 , September 2023, , Pages 161-170
Abstract
Lung cancer is caused by the irregular and uncontrolled growth of cancer cells in the lung tissue. Cancer cells find the ability to divide and increase in an irregular and uncoordinated manner. The result of this proliferation is the formation of a cancerous mass in the lung. Lung cancer can start in ...
Read More
Lung cancer is caused by the irregular and uncontrolled growth of cancer cells in the lung tissue. Cancer cells find the ability to divide and increase in an irregular and uncoordinated manner. The result of this proliferation is the formation of a cancerous mass in the lung. Lung cancer can start in different parts of the lung, such as the bronchi (the air tubes that connect to the lungs) or non-bronchial tissues, and quickly spread to other parts of the body. The precise understanding of the mechanism of lung cancer is still a complex issue and many researches are being conducted in this field. However, early diagnosis has an important impact on the disease treatment process. Therefore, in this research, the diagnosis and classification of this disease is discussed with the help of deep learning and learning transfer. In this regard, the pre-trained Alexnet network has been selected. During the process of transfer learning, the network for lung cancer detection is set on IQ-OTH/NCCD data in three categories: normal, benign and malignant. For this purpose, the last all-connection layer of the Alexnet network is removed and replaced by a new all-connection layer corresponding to the number of layers in the dataset. The classification accuracy of the proposed method on the IQ-OTH/NCCD dataset is reported to be 93%.
Biomedical Image Processing / Medical Image Processing
Mohammad Mahdi Alimoradi; Mohammad Bagher Khodabakhshi; Shahriar Jamasb
Volume 17, Issue 1 , May 2023, , Pages 61-70
Abstract
Stroke is one of the causes of death and the main cause of disability in developed countries. Normally, identification of stroke lesions is done by magnetic imaging, and its analysis requires the continuous presence of a doctor in the treatment center. Therefore, intelligent processing of medical images ...
Read More
Stroke is one of the causes of death and the main cause of disability in developed countries. Normally, identification of stroke lesions is done by magnetic imaging, and its analysis requires the continuous presence of a doctor in the treatment center. Therefore, intelligent processing of medical images will be an effective approach for automatic diagnosis of brain lesions.In this paper, a new integrated framework based on fuzzy inference system and deep neural network for automatic segmentation of brain lesions is introduced. In this regard, firstly, an improved U-net deep network (U-net) has been introduced for lesion detection and segmentation, which includes increasing the number of encoder and decoder layers along with changing the activation functions. Then, by using a fuzzy inference system based on if-then rules used by membership functions, the proposed approach of this study, which is based on the pre-processing of input images and the use of the unit network, has been introduced.The results showed that the integration of the fuzzy inference system in the pre-processing with the improved deep network could increase the DICE coefficient up to 0.84. In addition, improving the contrast of the input images by the fuzzy system compared to the usual pre-processing methods such as histogram equalization showed a much better performance in the detection of lesions with small dimensions, which is due to the ability to control the amount of contrast increase in the fuzzy systems compared to the usual methods.
Biomedical Image Processing / Medical Image Processing
Dorsa Jafarkhah Seighalani; Mehran Yazdi; Mohammad Faghihi
Volume 14, Issue 4 , February 2021, , Pages 267-276
Abstract
Cancer is one of the most common diseases at the present time. Among different types of this disease, brain cancer has a high fatality rate and accurate and timely diagnosis of it, can have a major impact on the patient’s life. Doctors need MRI and CT scan of brain to diagnose this condition. A ...
Read More
Cancer is one of the most common diseases at the present time. Among different types of this disease, brain cancer has a high fatality rate and accurate and timely diagnosis of it, can have a major impact on the patient’s life. Doctors need MRI and CT scan of brain to diagnose this condition. A precise image processing technique can help the medical specialists and speed up the diagnosis process. Many methods have been proposed to recognize brain tumors in medical images; however their accuracies were not acceptable. In fact, low accuracy is a result of the similarities between brain and tumor tissue. In this paper we propose a tumor recognition method using fusion of MRI and CT Scan images. This method exploits a deep learning based feature extraction algorithm that helps to distinguish tumors from brain tissue. Tumor recognition and accuracy calculation is performed for three common types of brain tumors (glioma, meningioma, and pituitary tumor). Our results show a great improvement of performance in comparison to related works.
Neural Network / Biological & Artificial Neural Network / BNN & ANN
Seyedeh Sadaf Razavinezhad; Amir mohammad Fallah; Seyed Abolghasem Mirroshandel
Volume 14, Issue 4 , February 2021, , Pages 307-320
Abstract
Diabetes is a common disease all around the world. It is a difficult and incurable but controllable disease, so it is important to control and prevent its complications. Thus, low error and smart methods are used to predict blood glucose levels and prevent dangerous complications to control it effectively. ...
Read More
Diabetes is a common disease all around the world. It is a difficult and incurable but controllable disease, so it is important to control and prevent its complications. Thus, low error and smart methods are used to predict blood glucose levels and prevent dangerous complications to control it effectively. In this regard, different methods were investigated. In this research, two models based on deep learning technique are used which produce efficient and optimal results. These models are composed of different combinations of long short-term memory and feed forward neural networks which predict the patient's future blood glucose levels with considerable accuracy and speed. To achieve more comprehensive model, 81,200 blood glucose data was evaluated through 203 patients. In addition, 27 effective features in patients' blood glucose levels are considered in this regard. Furthermore, cross-validation method which is suitable for time series was used for more accurate evaluation. The results showed that Autoregressive Integrated Moving Average model could not predict blood glucose levels considering this amount of data and system hardware limitations, while the models based on deep learning had good performance and good speed. Furthermore, the second proposed model for the prediction horizons of 5, 10, and 15 minutes outperformed the first one with 13.8%, 16%, and 18.9%, respectively. Therefore, the second proposed model can be more reliable for predicting blood glucose. So, it can be used in smart warning systems to prevent hypoglycemia, which is a dangerous and widespread problem of type 1 diabetes.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Raheleh Davoodi; Mohammad Hasan Moradi
Volume 12, Issue 1 , June 2018, , Pages 25-39
Abstract
Depression is one of the most common mental disorders in the current century where early diagnosis can result in better treatment. One of the depression diagnostic methods is the analysis of the brain electrical signals. In this paper, we are seeking for a method to distinguish among the levels of the ...
Read More
Depression is one of the most common mental disorders in the current century where early diagnosis can result in better treatment. One of the depression diagnostic methods is the analysis of the brain electrical signals. In this paper, we are seeking for a method to distinguish among the levels of the depression. The proposed model is a deep rule-based system based on the stacked principle and focuses on the interpretability of the rules alongside high accuracy. Fuzzy systems have the proper capability in the classification of medical data with various levels of uncertainty. Moreover, in the recent years, deep learning has been taken considerable attention in the field of Artificial Intelligence. In this paper, we aim to benefit from capabilities of both fields. The proposed architecture employs a robust fuzzy clustering approach that can determine an appropriate number of clusters in each layer, unsupervised and a hierarchical stacked structure to transfer the interpretable trained rules from the previous layers with the same linguistic labels to the next layer. The interpretability is due to the presence of the input space into the consequent ones. The presence of the output of the previous layer’s rules at the input space of the next parts equals to a fuzzy system with non-linear consequent or the certainty factor in a fuzzy system with linear consequent. EEG data were preprocessed and time, frequency and nonlinear features such as recurrent plot were extracted and selected and after that were employed in the proposed system. The proposed system was compared with common classifiers like Neural Net, Support Vector Machine, Naive Bayes, Decision Tree and Linear Discriminant Analysis. Accuracy results for the test data in 30 folds (49.01% in comparison to 41.42%, 40.47%, 40.01%, 38.35% and 40.28% respectively) demonstrate the considerable performance of the proposed system.
