Article
Computer Science, Artificial Intelligence
Hasnae Zerouaoui, Ali Idri, Omar El Alaoui
Summary: The progress in deep learning, machine learning, and digitization of pathology slides has shown great potential for more precise classification and prediction diagnosis of breast tumors. This paper investigates different deep learning architectures, machine learning models, and ensemble strategies for breast cancer classification using the BreakHis dataset. The proposed deep hybrid homogenous ensemble approach outperforms other methods, achieving high accuracy in classifying breast cancer images.
Article
Computer Science, Artificial Intelligence
Samriddha Majumdar, Payel Pramanik, Ram Sarkar
Summary: Breast cancer is the second deadliest disease among women globally. Histopathology image analysis is an effective method for detecting tumor malignancies. Computer-aided diagnosis (CAD) using convolutional neural network (CNN) models has shown potential in breast histopathological image classification, but there is room for improvement. This paper proposes a novel rank-based ensemble method that combines multiple CNN models to enhance classification accuracy.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Engineering, Biomedical
Ghulam Murtaza, Ainuddin Wahid Abdul Wahab, Ghulam Raza, Liyana Shuib
Summary: This paper introduces a tree-based breast tumor multiclassification model based on deep learning, aiming to solve multiclassification problems by extracting discriminative features and utilizing pretraining to prevent overfitting. Additionally, a misclassification reduction algorithm is implemented to enhance the model's performance, resulting in superior classification accuracy compared to existing baseline studies.
COMPUTERIZED MEDICAL IMAGING AND GRAPHICS
(2021)
Article
Computer Science, Information Systems
Maila L. Claro, Rodrigo de M. S. Veras, Andre M. Santana, Luis Henrique S. Vogado, Geraldo Braz Junior, Fatima N. S. de Medeiros, Joao Manuel R. S. Tavares
Summary: This article evaluates the impact of widely applied techniques, including data augmentation and multilevel and ensemble configurations, in deep learning-based computer-aided diagnosis systems. The findings suggest that data augmentation techniques improve the performance of convolutional neural networks, and a combination of CNNs also enhances the classification results.
INFORMATION SCIENCES
(2022)
Article
Engineering, Biomedical
Sepideh Khaliliboroujeni, Xiangjian He, Wenjing Jia, Saeed Amirgholipour
Summary: Breast cancer is one of the most prevalent and fatal diseases among women worldwide. This study develops a CAD system for metastasis detection in breast cancer, using only a small amount of annotated data with lower resolution, providing more accurate and faster results.
COMPUTERIZED MEDICAL IMAGING AND GRAPHICS
(2022)
Article
Medicine, General & Internal
Arnab Bagchi, Payel Pramanik, Ram Sarkar
Summary: Breast cancer is a deadly disease that affects women worldwide. Early diagnosis and proper treatment can save lives. Breast image analysis, including histopathological image analysis, and computer-aided diagnosis, can help improve efficiency and accuracy in breast cancer detection. In this study, a deep learning-based method was developed to classify breast cancer using histopathological images, achieving high classification accuracy.
Article
Radiology, Nuclear Medicine & Medical Imaging
Mahati Munikoti Srikantamurthy, V. P. Subramanyam Rallabandi, Dawood Babu Dudekula, Sathishkumar Natarajan, Junhyung Park
Summary: This study proposes a deep learning model for automatic classification of breast cancer subtypes. The model combines convolutional neural network (CNN) and long short-term memory recurrent neural network (LSTM RNN), and uses a transfer learning approach in the evaluation on the BreakHis dataset. The results show that the proposed model achieves high accuracy in both binary and multi-class classification tasks.
BMC MEDICAL IMAGING
(2023)
Review
Chemistry, Analytical
Yue Zhao, Jie Zhang, Dayu Hu, Hui Qu, Ye Tian, Xiaoyu Cui
Summary: This study analyzed the detection, segmentation, and classification of breast cancer in pathological images using statistical methods. It found that deep learning has significant capabilities in the application of breast cancer pathological images, and in certain circumstances, its accuracy surpasses that of pathologists.
Article
Computer Science, Information Systems
Anjali Gautam
Summary: Breast cancer, a deadly disease primarily affecting women, is spreading globally with increasing cases. Manual detections suffer from high false-positive rates due to human error and time-consuming processes. Early detection is crucial to prevent fatality, hence the emergence of machine learning and deep learning approaches. This paper focuses on deep learning models for breast cancer detection, providing an in-depth study and analysis of mammography, histopathology, and ultrasound datasets. It encompasses a wider range of methods and recent works, including the use of vision transformers, serving as a valuable resource for researchers in the field.
MULTIMEDIA SYSTEMS
(2023)
Article
Computer Science, Hardware & Architecture
Kadir Can Burcak, Omer Kaan Baykan, Harun Uguz
Summary: Deep learning algorithms have shown remarkable results in medical diagnosis and image analysis, with a particular focus on breast cancer histopathological images. The proposed deep convolutional neural network model, utilizing various algorithms, achieved an accuracy value of up to 99.05% in classification, reducing the burden on pathologists and improving diagnostic objectivity.
JOURNAL OF SUPERCOMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Hela Elmannai, Monia Hamdi, Abeer AlGarni
Summary: Breast cancer is a leading cause of death among women worldwide, and early diagnosis is crucial. This research focuses on using deep learning to classify breast cancer histopathological images, with experiments showing that the proposed method outperforms previous ones in terms of accuracy and sensitivity.
INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Meghavi Rana, Megha Bhushan
Summary: This study demonstrates the accuracy of transfer learning-based deep learning methods in Computer-Aided Design (CAD) systems for the early detection and analysis of diseases such as lung cancer, brain tumor, and breast cancer. By utilizing pre-trained models, the time for deep learning-based tasks in computer vision can be reduced. The effectiveness of transfer learning models for tumor classification is explained, and the best performing models on a specific dataset are identified.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Lingxiao Li, Niantao Xie, Sha Yuan
Summary: Quantities and diversities of datasets are essential for training models in medical image diagnosis. However, problems arise in real scenes due to insufficient availability of required data in a single institution and the inability to share patient data due to privacy regulations. To address these issues, we propose a federated learning framework that allows knowledge fusion by sharing model parameters rather than data. Experimental results based on the BreakHis dataset demonstrate the feasibility and efficiency of the proposed framework.
Article
Computer Science, Artificial Intelligence
Abtin Riasatian, Morteza Babaie, Danial Maleki, Shivam Kalra, Mojtaba Valipour, Sobhan Hemati, Manit Zaveri, Amir Safarpoor, Sobhan Shafiei, Mehdi Afshari, Maral Rasoolijaberi, Milad Sikaroudi, Mohd Adnan, Sultaan Shah, Charles Choi, Savvas Damaskinos, Clinton Jv Campbell, Phedias Diamandis, Liron Pantanowitz, Hany Kashani, Ali Ghodsi, H. R. Tizhoosh
Summary: Pre-trained deep artificial neural networks are a dominant source for image representation and their performance in image analysis can be improved through fine-tuning. This study introduces a new network, KimiaNet, which outperforms the original DenseNet and other networks in representing histopathology images. By utilizing a large dataset and training with different configurations, KimiaNet shows superior results in image analysis.
MEDICAL IMAGE ANALYSIS
(2021)
Article
Computer Science, Artificial Intelligence
Sevinc Ilhan Omurca, Ekin Ekinci, Semih Sevim, Eren Berk Edinc, Suleyman Eken, Ahmet Sayar
Summary: Artificial Intelligence technologies are being increasingly used in various systems, such as online document tracking systems. This study proposes three techniques to efficiently classify student documents uploaded in PDF format, improving the reliability and efficiency of the system.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Alaa S. Al-Waisy, Shumoos Al-Fahdawi, Mazin Abed Mohammed, Karrar Hameed Abdulkareem, Salama A. Mostafa, Mashael S. Maashi, Muhammad Arif, Begonya Garcia-Zapirain
Summary: The outbreaks of the COVID-19 epidemic have increased the pressure on healthcare and medical systems worldwide. Chest radiography imaging has been shown to be an effective screening technique for diagnosing the COVID-19 epidemic. To reduce pressure on radiologists, a hybrid deep learning framework called COVID-CheXNet has been developed for fast and accurate diagnosis of COVID-19 virus in chest X-ray images. The system achieved high detection accuracy and efficiency, making it a potential tool for real clinical centers.
Article
Chemistry, Analytical
Mazhar Javed Awan, Mohd Shafry Mohd Rahim, Naomie Salim, Amjad Rehman, Begonya Garcia-Zapirain
Summary: This study applies deep learning to automatically segment ACL tears from MRI images. By using the U-Net architecture and semantic segmentation technique, high accuracy segmentation results have been achieved. The method shows promising potential in the field of medical image analysis.
Article
Chemistry, Analytical
Sofia Zahia, Begonya Garcia-Zapirain, Jon Anakabe, Joan Ander, Oscar Jossa Bastidas, Alberto Loizate Totoricaguena
Summary: This study presents a comparative analysis of three different 3D scanning modalities for acquiring 3D meshes of stoma barrier rings from ostomized patients. The results show that the low-cost Structure Sensor structured light 3D sensor has great potential for such applications.
Article
Dermatology
S. Oukil, R. Kasmi, K. Mokrani, B. Garcia-Zapirain
Summary: The study proposes a novel algorithm for discriminating melanoma and benign skin lesions based on color and texture features, achieving high sensitivity (99.25%), specificity (99.58%), and accuracy (99.51%) on the PH2 dataset, outperforming existing methods.
SKIN RESEARCH AND TECHNOLOGY
(2022)
Article
Computer Science, Information Systems
Danyal Maheshwari, Daniel Sierra-Sosa, Begonya Garcia-Zapirain
Summary: This article presents the application of a Variational Quantum Classifier (VQC) for binary classification. The study demonstrates the effectiveness of amplitude encoding in enhancing prediction accuracy when applying the VQC method, and achieves high accuracies on various datasets.
Article
Health Care Sciences & Services
Hanane Allioui, Mazin Abed Mohammed, Narjes Benameur, Belal Al-Khateeb, Karrar Hameed Abdulkareem, Begonya Garcia-Zapirain, Robertas Damasevicius, Rytis Maskeliunas
Summary: In this study, a new mask extraction method based on multi-agent deep reinforcement learning (DRL) was introduced and applied to the diagnosis of COVID-19. Experimental validation showed that the method can accurately extract masks of COVID-19 infected areas and achieved good results in pathogenic diagnostic tests and time saving.
JOURNAL OF PERSONALIZED MEDICINE
(2022)
Article
Computer Science, Artificial Intelligence
Georgina Curto, Mario Fernando Jojoa Acosta, Flavio Comim, Begona Garcia-Zapirain
Summary: This article discusses bias in artificial intelligence, particularly against the poor. The results of the study demonstrate the existence of bias and provide relevant data. Additionally, the evidence shows that bias has important implications for human development in developing countries.
Article
Multidisciplinary Sciences
Zabit Hameed, Begonya Garcia-Zapirain, Jose Javier Aguirre, Mario Arturo Isaza-Ruget
Summary: This paper proposes a deep learning approach for automatic classification of breast cancer microscopy images, achieving good results. The study found that the performance was better on the original dataset, and stain normalization techniques could not surpass the results of the original dataset.
SCIENTIFIC REPORTS
(2022)
Review
Environmental Sciences
S. Shapoval, Merce Gimeno-Santos, Amaia Mendez Zorrilla, Begona Garcia-Zapirain, Myriam Guerra-Balic, Sara Signo-Miguel, Olga Bruna-Rabassa
Summary: This study aims to analyze the solutions found in executive function (EF) training for adults with intellectual disability (ID) in recent years, evaluate them with key parameters, and identify the features and issues in the further development of their system. The results revealed that planning and decision-making were the most frequently mentioned EFs in the analyzed studies, followed by working memory and social cognition, behavioral regulation, flexibility, and inhibition capacity. The trend analysis showed improvements in EFs, although not significant. This study provides significant insights into the creation of support systems and program execution.
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
(2022)
Review
Computer Science, Information Systems
Cyrille YetuYetu Kesiku, Andrea Chaves-Villota, Begonya Garcia-Zapirain
Summary: This article discusses the importance of biomedical literature classification and related problems. By analyzing a large number of literature, different classification methods and challenges are understood. The study found that data-centric challenges and data quality challenges are the key issues currently faced in biomedical text classification.
Article
Biology
Mazin Abed Mohammed, Abdullah Lakhan, Karrar Hameed Abdulkareem Begona Garcia-Zapirain, Begona Garcia-Zapirain
Summary: Recently, there has been an increasing ratio of cancer diseases among patients with many cases reported in different clinical hospitals. This paper explores different types of cancer by analyzing, classifying, and processing multi-omics dataset in a fog cloud network. The study proposes new hybrid cancer detection schemes based on SARSA on-policy and multi-omics workload learning.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Genetics & Heredity
Ali Raza, Furqan Rustam, Hafeez Ur Rehman Siddiqui, Isabel de la Torre Diez, Begona Garcia-Zapirain, Ernesto Lee, Imran Ashraf
Summary: This study focuses on predicting genetic disorders using artificial intelligence-based methods. It proposes a novel feature engineering approach and classifier chain approach, and evaluates the performance using multiple evaluation metrics. Results show that extreme gradient boosting (XGB) outperforms state-of-the-art approaches in terms of both performance and computational complexity.
Article
Computer Science, Information Systems
Oscar Jossa-Bastidas, Ainhoa Osa Sanchez, Leire Bravo-Lamas, Begonya Garcia-Zapirain
Summary: Gluten is a natural complex protein found in various cereal grains and can cause immune system attacks in individuals with celiac disease. There are multiple methods for detecting gluten, but this study focuses on developing a novel IoT system using Near-infrared spectroscopy technology and AI algorithms. The results showed high accuracy in predicting the presence or absence of gluten in flour samples.
Review
Medicine, General & Internal
Mazin Abed Mohammed, Karrar Hameed Abdulkareem, Ahmed M. Dinar, Begonya Garcia Zapirain
Summary: This research reviews and evaluates relevant scientific studies on deep learning (DL) models in the omics field, demonstrating their potential and identifying key challenges. The literature survey includes clinical applications, datasets, and highlights the difficulties faced by researchers. Using a systematic approach and specified criteria, 65 articles were selected from four search engines, covering clinical applications, review publications, and comparative analysis guidelines. Obstacles related to DL, preprocessing procedures, datasets, model validation, and testbed applications were identified in the study. The research provides valuable insights and guidance for practitioners in omics data analysis using DL.