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Intelligent Biomedical Image Processing and Computer Vision

Submission Deadline: 30 June 2023 (closed) View: 187

Guest Editors

Prof. Deepak Gupta, Maharaja Agrasen Institute of Technology, India
Prof. Oscar Castillo, Tijuana Institute of Technology, Mexico
Prof. Utku Kose, Suleyman Demirel University, Turkey

Summary

Today the human lives in the age of Information and technology. Information is the key, the power, and the engine that moves the world’s economy. The world is moving with markets data, medical epidemiologic sets, Internet browsing records, geological surveys data, complex engineering models, and so on. Health Sciences are fully embedded in information technology. Health science and Biology are very complex fields and have made a long walk from the ancient times, but processes involved in biology, medicine and physiology are much too intricate to be faithfully modeled. In the early eighties, AI in medicine was the main concern while developing medical expert systems in specialized medical domains aimed at supporting diagnostic decision-making. The main problems addressed at this early stage of expert system research concerned knowledge acquisition, knowledge representation, reasoning and explanation. Now there are many modern hospitals and health care institutions, which are well equipped with monitoring and other advanced data collection devices. The need of knowledge on the domain or on the data analysis process becomes essential in biomedical applications, as medical decision making needs to be supported by arguments based on basic medical and pharmacological knowledge.

 

The overall aim of this special issue is to collect state-of-the-art contributions on the latest research and development, up-to-date issues, and challenges in the field of Intelligent Biomedical Image Processing and Computer Vision and related applications. Proposed submissions should be original, unpublished, and present novel in-depth fundamental research contributions either from a methodological perspective or from an application point of view.

 

The topics of interest are strictly limited to:

• Computational intelligence in biological and clinical medicine

• Behavioral, Environmental, and Public health informatics

• Biological network modeling and analysis

• Biomedical imaging and data visualization

• Evolutionary algorithms for optimization methodologies for biomedical applications

• Data mining for health data processing and analysis on mobile devices

• Machine learning and deep learning for health-related mobile applications

• Intelligent medical information systems

• Predictive modeling and analytics in healthcare

• Virtual and augmented reality

• Medical image/signal analysis and processing

• Internet of health things

• Biomedical data pattern recognition


Published Papers

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  • Open Access

    ARTICLE

    Robust Facial Biometric Authentication System Using Pupillary Light Reflex for Liveness Detection of Facial Images

    Puja S. Prasad, Adepu Sree Lakshmi, Sandeep Kautish, Simar Preet Singh, Rajesh Kumar Shrivastava, Abdulaziz S. Almazyad, Hossam M. Zawbaa, Ali Wagdy Mohamed
    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 725-739, 2024, DOI:10.32604/cmes.2023.030640
    （This article belongs to the Special Issue: Intelligent Biomedical Image Processing and Computer Vision)
    Abstract Pupil dynamics are the important characteristics of face spoofing detection. The face recognition system is one of the most used biometrics for authenticating individual identity. The main threats to the facial recognition system are different types of presentation attacks like print attacks, 3D mask attacks, replay attacks, etc. The proposed model uses pupil characteristics for liveness detection during the authentication process. The pupillary light reflex is an involuntary reaction controlling the pupil’s diameter at different light intensities. The proposed framework consists of two-phase methodologies. In the first phase, the pupil’s diameter is calculated by applying… More >

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    612

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    307

  • Open Access

    ARTICLE

    Multi-Level Parallel Network for Brain Tumor Segmentation

    Juhong Tie, Hui Peng
    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 741-757, 2024, DOI:10.32604/cmes.2023.043353
    （This article belongs to the Special Issue: Intelligent Biomedical Image Processing and Computer Vision)
    Abstract Accurate automatic segmentation of gliomas in various sub-regions, including peritumoral edema, necrotic core, and enhancing and non-enhancing tumor core from 3D multimodal MRI images, is challenging because of its highly heterogeneous appearance and shape. Deep convolution neural networks (CNNs) have recently improved glioma segmentation performance. However, extensive down-sampling such as pooling or stridden convolution in CNNs significantly decreases the initial image resolution, resulting in the loss of accurate spatial and object parts information, especially information on the small sub-region tumors, affecting segmentation performance. Hence, this paper proposes a novel multi-level parallel network comprising three different… More >

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    497

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    287

  • Open Access

    ARTICLE

    Early Detection of Colletotrichum Kahawae Disease in Coffee Cherry Based on Computer Vision Techniques

    Raveena Selvanarayanan, Surendran Rajendran, Youseef Alotaibi
    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 759-782, 2024, DOI:10.32604/cmes.2023.044084
    （This article belongs to the Special Issue: Intelligent Biomedical Image Processing and Computer Vision)
    Abstract Colletotrichum kahawae (Coffee Berry Disease) spreads through spores that can be carried by wind, rain, and insects affecting coffee plantations, and causes 80% yield losses and poor-quality coffee beans. The deadly disease is hard to control because wind, rain, and insects carry spores. Colombian researchers utilized a deep learning system to identify CBD in coffee cherries at three growth stages and classify photographs of infected and uninfected cherries with 93% accuracy using a random forest method. If the dataset is too small and noisy, the algorithm may not learn data patterns and generate accurate predictions.… More >

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    583

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    309

  • Open Access

    ARTICLE

    A Hybrid Classification and Identification of Pneumonia Using African Buffalo Optimization and CNN from Chest X-Ray Images

    Nasser Alalwan, Ahmed I. Taloba, Amr Abozeid, Ahmed Ibrahim Alzahrani, Ali H. Al-Bayatti
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2497-2517, 2024, DOI:10.32604/cmes.2023.029910
    （This article belongs to the Special Issue: Intelligent Biomedical Image Processing and Computer Vision)
    Abstract An illness known as pneumonia causes inflammation in the lungs. Since there is so much information available from various X-ray images, diagnosing pneumonia has typically proven challenging. To improve image quality and speed up the diagnosis of pneumonia, numerous approaches have been devised. To date, several methods have been employed to identify pneumonia. The Convolutional Neural Network (CNN) has achieved outstanding success in identifying and diagnosing diseases in the fields of medicine and radiology. However, these methods are complex, inefficient, and imprecise to analyze a big number of datasets. In this paper, a new hybrid… More >

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    643

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    811

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    2

  • Open Access

    ARTICLE

    Optimizing Deep Learning for Computer-Aided Diagnosis of Lung Diseases: An Automated Method Combining Evolutionary Algorithm, Transfer Learning, and Model Compression

    Hassen Louati, Ali Louati, Elham Kariri, Slim Bechikh
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2519-2547, 2024, DOI:10.32604/cmes.2023.030806
    （This article belongs to the Special Issue: Intelligent Biomedical Image Processing and Computer Vision)
    Abstract Recent developments in Computer Vision have presented novel opportunities to tackle complex healthcare issues, particularly in the field of lung disease diagnosis. One promising avenue involves the use of chest X-Rays, which are commonly utilized in radiology. To fully exploit their potential, researchers have suggested utilizing deep learning methods to construct computer-aided diagnostic systems. However, constructing and compressing these systems presents a significant challenge, as it relies heavily on the expertise of data scientists. To tackle this issue, we propose an automated approach that utilizes an evolutionary algorithm (EA) to optimize the design and compression More >

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    675

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    858

  • Open Access

    ARTICLE

    Robust Machine Learning Technique to Classify COVID-19 Using Fusion of Texture and Vesselness of X-Ray Images

    Shaik Mahaboob Basha, Victor Hugo C. de Albuquerque, Samia Allaoua Chelloug, Mohamed Abd Elaziz, Shaik Hashmitha Mohisin, Suhail Parvaze Pathan
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1981-2004, 2024, DOI:10.32604/cmes.2023.031425
    （This article belongs to the Special Issue: Intelligent Biomedical Image Processing and Computer Vision)
    Abstract Manual investigation of chest radiography (CXR) images by physicians is crucial for effective decision-making in COVID-19 diagnosis. However, the high demand during the pandemic necessitates auxiliary help through image analysis and machine learning techniques. This study presents a multi-threshold-based segmentation technique to probe high pixel intensity regions in CXR images of various pathologies, including normal cases. Texture information is extracted using gray co-occurrence matrix (GLCM)-based features, while vessel-like features are obtained using Frangi, Sato, and Meijering filters. Machine learning models employing Decision Tree (DT) and Random Forest (RF) approaches are designed to categorize CXR images… More >

    Graphic Abstract

    Robust Machine Learning Technique to Classify COVID-19 Using Fusion of Texture and Vesselness of X-Ray Images

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    670

  • Open Access

    REVIEW

    A Systematic Review on the Internet of Medical Things: Techniques, Open Issues, and Future Directions

    Apurva Sonavane, Aditya Khamparia, Deepak Gupta
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1525-1550, 2023, DOI:10.32604/cmes.2023.028203
    （This article belongs to the Special Issue: Intelligent Biomedical Image Processing and Computer Vision)
    Abstract IoT usage in healthcare is one of the fastest growing domains all over the world which applies to every age group. Internet of Medical Things (IoMT) bridges the gap between the medical and IoT field where medical devices communicate with each other through a wireless communication network. Advancement in IoMT makes human lives easy and better. This paper provides a comprehensive detailed literature survey to investigate different IoMT-driven applications, methodologies, and techniques to ensure the sustainability of IoMT-driven systems. The limitations of existing IoMT frameworks are also analyzed concerning their applicability in real-time driven systems More >

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    1346

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    596

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    1

  • Open Access

    ARTICLE

    Rectal Cancer Stages T2 and T3 Identification Based on Asymptotic Hybrid Feature Maps

    Shujing Sun, Jiale Wu, Jian Yao, Yang Cheng, Xin Zhang, Zhihua Lu, Pengjiang Qian
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 923-938, 2023, DOI:10.32604/cmes.2023.027356
    （This article belongs to the Special Issue: Intelligent Biomedical Image Processing and Computer Vision)
    Abstract Many existing intelligent recognition technologies require huge datasets for model learning. However, it is not easy to collect rectal cancer images, so the performance is usually low with limited training samples. In addition, traditional rectal cancer staging is time-consuming, error-prone, and susceptible to physicians’ subjective awareness as well as professional expertise. To settle these deficiencies, we propose a novel deep-learning model to classify the rectal cancer stages of T2 and T3. First, a novel deep learning model (RectalNet) is constructed based on residual learning, which combines the squeeze-excitation with the asymptotic output layer and new More >

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    970

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    538

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    1

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