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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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    A deep learning approach for robust, multi-oriented, and curved text detection
    (SPRINGER, 2022) Ranjbarzadeh, Ramin; Jafarzadeh Ghoushchi, Saeid; Anari, Shokofeh; Safavi, Sadaf; Tataei Sarshar, Nazanin; Babaee Tirkolaee, Erfan; Bendechache, Malika
    Automatic text localization and segmentation in a normal environment with vertical or curved texts are core elements of numerous tasks comprising the identification of vehicles and self-driving cars, and preparing significant information from real scenes to visually impaired people. Nevertheless, texts in the real environment can be discovered with a high level of angles, profiles, dimensions, and colors which is an arduous process to detect. In this paper, a new framework based on a convolutional neural network (CNN) is introduced to obtain high efficiency in detecting text even in the presence of a complex background. Due to using a new inception layer and an improved ReLU layer, an excellent result is gained to detect text even in the presence of complex backgrounds. At first, four new m.ReLU layers are employed to explore low-level visual features. The new m.ReLU building block and inception layer are optimized to detect vital information maximally. The effect of stacking up inception layers (kernels with the dimension of 3 x 3 or bigger) is explored and it is demonstrated that this strategy is capable of obtaining mostly varying-sized texts further successfully than a linear chain of convolution layers (Conv layers). The suggested text detection algorithm is conducted in four well-known databases, namely ICDAR 2013, ICDAR 2015, ICDAR 2017, and ICDAR 2019. Text detection results on all mentioned databases with the highest recall of 94.2%, precision of 95.6%, and F-score of 94.8% illustrate that the developed strategy outperforms the state-of-the-art frameworks.
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    ME-CCNN: Multi-encoded images and a cascade convolutional neural network for breast tumor segmentation and recognition
    (Springer, 2023) Ranjbarzadeh, Ramin; Jafarzadeh Ghoushchi, Saeid; Tataei Sarshar, Nazanin; Tirkolaee, Erfan Babaee; Ali, Sadia Samar; Kumar, Teerath; Bendechache, Malika
    Breast tumor segmentation and recognition from mammograms play a key role in healthcare and treatment services. As different tumors in mammography have dissimilar densities, shapes, sizes, and edges, the interpretation of mammograms can be time-consuming and prone to interpretation variability even for a highly trained radiologist or expert. In this study, several encoding approaches are first proposed to achieve an effective breast cancer recognition system as well as create new images from the input image. Each encoded image represents some unique features that are crucial for detecting the target texture properly. Subsequently, pectoral muscle is eliminated using obtained features from these encoded images. Moreover, 11 distinct images are then applied to a shallow and efficient cascade Convolutional Neural Network (CNN) for classifying each pixel inside the image. This network accepts 11 local patches as the input from 11 obtained encoded images. Next, all extracted features are concatenated to a vertical vector to apply to the fully connected layers. Using different representations of the input mammogram images, the suggested model is able to analyze the input texture more effectively without using a deep CNN model. Finally, comprehensive experiments are then conducted on two public datasets which then demonstrate that the proposed framework successfully is able to gain competitive outcomes compared to a number of baselines.