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    Brain tumor segmentation based on optimized convolutional neural network and improved chimp optimization algorithm
    (Pergamon-Elsevier Science Ltd, 2024) Ranjbarzadeh, Ramin; Zarbakhsh, Payam; Caputo, Annalina; Tirkolaee, Erfan Babaee; Bendechache, Malika
    Reliable and accurate brain tumor segmentation is a challenging task even with the appropriate acquisition of brain images. Tumor grading and segmentation utilizing Magnetic Resonance Imaging (MRI) are necessary steps for correct diagnosis and treatment planning. There are different MRI sequence images (T1, Flair, T1ce, T2, etc.) for identifying different parts of the tumor. Due to the diversity in the illumination of each brain imaging modality, different information and details can be obtained from each input modality. Therefore, by using various MRI modalities, the diagnosis system is capable of finding more unique details that lead to a better segmentation result, especially in fuzzy borders. In this study, to achieve an automatic and robust brain tumor segmentation framework using four MRI sequence images, an optimized Convolutional Neural Network (CNN) is proposed. All weight and bias values of the CNN model are adjusted using an Improved Chimp Optimization Algorithm (IChOA). In the first step, all four input images are normalized to find some potential areas of the existing tumor. Next, by employing the IChOA, the best features are selected using a Support Vector Machine (SVM) classifier. Finally, the best-extracted features are fed to the optimized CNN model to classify each object for brain tumor segmentation. Accordingly, the proposed IChOA is utilized for feature selection and optimizing Hyperparameters in the CNN model. The experimental outcomes conducted on the BRATS 2018 dataset demonstrate superior performance (Precision of 97.41 %, Recall of 95.78 %, and Dice Score of 97.04 %) compared to the existing frameworks.
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    Brain tumor segmentation of MRI images: a comprehensive review on the application of artificial intelligence tools
    (Elsevier, 2023) Ranjbarzadeh, Ramin; Caputo, Annalina; Tirkolaee, Erfan Babaee; Jafarzadeh Ghoushchi, Saeid; Bendechache, Malika
    Background: Brain cancer is a destructive and life-threatening disease that imposes immense negative effects on patients’ lives. Therefore, the detection of brain tumors at an early stage improves the impact of treatments and increases the patients survival rates. However, detecting brain tumors in their initial stages is a demanding task and an unmet need. Methods: The present study presents a comprehensive review of the recent Artificial Intelligence (AI) methods of diagnosing brain tumors using MRI images. These AI techniques can be divided into Supervised, Unsupervised, and Deep Learning (DL) methods. Results: Diagnosing and segmenting brain tumors usually begin with Magnetic Resonance Imaging (MRI) on the brain since MRI is a noninvasive imaging technique. Another existing challenge is that the growth of technology is faster than the rate of increase in the number of medical staff who can employ these technologies. It has resulted in an increased risk of diagnostic misinterpretation. Therefore, developing robust automated brain tumor detection techniques has been studied widely over the past years. Conclusion: The current review provides an analysis of the performance of modern methods in this area. Moreover, various image segmentation methods in addition to the recent efforts of researchers are summarized. Finally, the paper discusses open questions and suggests directions for future research. © 2022 Elsevier Ltd
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    Breast tumor localization and segmentation using machine learning techniques: overview of datasets, findings, and methods
    (Elsevier, 2023) Ranjbarzadeh, Ramin; Dorosti, Shadi; Jafarzadeh Ghoushchi, Saeid; Caputo, Annalina; Tirkolaee, Erfan Babaee; Ali, Sadia Samar; Arshadi, Zahra; Bendechache, Malika
    The Global Cancer Statistics 2020 reported breast cancer (BC) as the most common diagnosis of cancer type. Therefore, early detection of such type of cancer would reduce the risk of death from it. Breast imaging techniques are one of the most frequently used techniques to detect the position of cancerous cells or suspicious lesions. Computer-aided diagnosis (CAD) is a particular generation of computer systems that assist experts in detecting medical image abnormalities. In the last decades, CAD has applied deep learning (DL) and machine learning approaches to perform complex medical tasks in the computer vision area and improve the ability to make decisions for doctors and radiologists. The most popular and widely used technique of image processing in CAD systems is segmentation which consists of extracting the region of interest (ROI) through various techniques. This research provides a detailed description of the main categories of segmentation procedures which are classified into three classes: supervised, unsupervised, and DL. The main aim of this work is to provide an overview of each of these techniques and discuss their pros and cons. This will help researchers better understand these techniques and assist them in choosing the appropriate method for a given use case. © 2022 Elsevier Ltd
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    ETACM: an encoded-texture active contour model for image segmentation with fuzzy boundaries
    (Springer, 2023) Ranjbarzadeh, Ramin; Sadeghi, Soroush; Fadaeian, Aida; Ghoushchi, Saeid Jafarzadeh; Tirkolaee, Erfan Babaee; Caputo, Annalina; Bendechache, Malika
    Active contour models (ACMs) have been widely used in image segmentation to segment objects. However, when it comes to segmenting images with severe intensity inhomogeneity, most current frameworks do not perform well, which can make it difficult to achieve the desired results. To address this issue, a decision-making model is proposed, which involves using enhanced local direction pattern (ELDP) and local directional number pattern (LDNP) texture descriptors to create an encoded-texture ACM. The principal component analysis (PCA) is then used to optimize the two encoded images and reduce the correlations before they are fused. To further improve the performance of the encoded-texture ACM, a function of minimizing energy globally (FMEG) is suggested by applying the vector-valued exploration technique from a non-convex surface to region-based ACMs. This approach enables the development of a model capable of directly building complex decision boundaries. The experimental results show that the proposed encoded-texture ACM outperforms many recent frameworks in terms of robustness and accuracy for segmenting images with intensity inhomogeneity, fuzzy boundaries, and noise. Therefore, the suggested approach provides a more effective and efficient solution to the problem of image segmentation, particularly for challenging images.