Kaustubh Arun Bhavsar¹, Ahed Abugabah², Jimmy Singla^1,*, Ahmad Ali AlZubi³, Ali Kashif Bashir⁴, Nikita⁵

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1997-2014, 2021, DOI:10.32604/cmc.2021.014943 - 05 February 2021

Abstract The unavailability of sufficient information for proper diagnosis, incomplete or miscommunication between patient and the clinician, or among the healthcare professionals, delay or incorrect diagnosis, the fatigue of clinician, or even the high diagnostic complexity in limited time can lead to diagnostic errors. Diagnostic errors have adverse effects on the treatment of a patient. Unnecessary treatments increase the medical bills and deteriorate the health of a patient. Such diagnostic errors that harm the patient in various ways could be minimized using machine learning. Machine learning algorithms could be used to diagnose various diseases with high… More >

Yasir Shahzad^1,*, Huma Javed¹, Haleem Farman², Jamil Ahmad², Bilal Jan³, Abdelmohsen A. Nassani⁴

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2463-2480, 2021, DOI:10.32604/cmc.2021.014904 - 05 February 2021

Abstract Smart healthcare integrates an advanced wave of information technology using smart devices to collect health-related medical science data. Such data usually exist in unstructured, noisy, incomplete, and heterogeneous forms. Annotating these limitations remains an open challenge in deep learning to classify health conditions. In this paper, a long short-term memory (LSTM) based health condition prediction framework is proposed to rectify imbalanced and noisy data and transform it into a useful form to predict accurate health conditions. The imbalanced and scarce data is normalized through coding to gain consistency for accurate results using synthetic minority oversampling… More >

Muhammad Kashif¹, Ayyaz Hussain², Asim Munir¹, Abdul Basit Siddiqui³, Aaqif Afzaal Abbasi⁴, Muhammad Aakif⁵, Arif Jamal Malik⁴, Fayez Eid Alazemi⁶, Oh-Young Song^7,*

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2123-2139, 2021, DOI:10.32604/cmc.2021.014782 - 05 February 2021

Abstract Expression detection plays a vital role to determine the patient’s condition in healthcare systems. It helps the monitoring teams to respond swiftly in case of emergency. Due to the lack of suitable methods, results are often compromised in an unconstrained environment because of pose, scale, occlusion and illumination variations in the image of the face of the patient. A novel patch-based multiple local binary patterns (LBP) feature extraction technique is proposed for analyzing human behavior using facial expression recognition. It consists of three-patch [TPLBP] and four-patch LBPs [FPLBP] based feature engineering respectively. Image representation is… More >

Jae Dong Lee^1,2, Hyo Soung Cha¹, Shailendra Rathore², Jong Hyuk Park^2,*

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1537-1553, 2021, DOI:10.32604/cmc.2021.014774 - 05 February 2021

Abstract In recent years, the application of a smart city in the healthcare sector via loT systems has continued to grow exponentially and various advanced network intrusions have emerged since these loT devices are being connected. Previous studies focused on security threat detection and blocking technologies that rely on testbed data obtained from a single medical IoT device or simulation using a well-known dataset, such as the NSL-KDD dataset. However, such approaches do not reflect the features that exist in real medical scenarios, leading to failure in potential threat detection. To address this problem, we proposed… More >

Saleh Albahli¹, Tahira Nazir^2,*, Aun Irtaza², Ali Javed³

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1333-1351, 2021, DOI:10.32604/cmc.2021.014691 - 05 February 2021

Abstract Diabetes is a metabolic disorder that results in a retinal complication called diabetic retinopathy (DR) which is one of the four main reasons for sightlessness all over the globe. DR usually has no clear symptoms before the onset, thus making disease identification a challenging task. The healthcare industry may face unfavorable consequences if the gap in identifying DR is not filled with effective automation. Thus, our objective is to develop an automatic and cost-effective method for classifying DR samples. In this work, we present a custom Faster-RCNN technique for the recognition and classification of DR… More >

Rabia Javed^1,2, Mohd Shafry Mohd Rahim¹, Tanzila Saba³, Suliman Mohamed Fati³, Amjad Rehman^3,*, Usman Tariq⁴

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2337-2352, 2021, DOI:10.32604/cmc.2021.014677 - 05 February 2021

Abstract Most of the melanoma cases of skin cancer are the life-threatening form of cancer. It is prevalent among the Caucasian group of people due to their light skin tone. Melanoma is the second most common cancer that hits the age group of 15–29 years. The high number of cases has increased the importance of automated systems for diagnosing. The diagnosis should be fast and accurate for the early treatment of melanoma. It should remove the need for biopsies and provide stable diagnostic results. Automation requires large quantities of images. Skin lesion datasets contain various kinds… More >

Daniel Sierra-Sosa^1,*, Juan D. Arcila-Moreno², Begonya Garcia-Zapirain³, Adel Elmaghraby¹

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1849-1861, 2021, DOI:10.32604/cmc.2021.013196 - 05 February 2021

Abstract Quantum Machine Learning (QML) techniques have been recently attracting massive interest. However reported applications usually employ synthetic or well-known datasets. One of these techniques based on using a hybrid approach combining quantum and classic devices is the Variational Quantum Classifier (VQC), which development seems promising. Albeit being largely studied, VQC implementations for “real-world” datasets are still challenging on Noisy Intermediate Scale Quantum devices (NISQ). In this paper we propose a preprocessing pipeline based on Stokes parameters for data mapping. This pipeline enhances the prediction rates when applying VQC techniques, improving the feasibility of solving classification More >

Wajdi Alhakami¹, Abdullah Baz², Hosam Alhakami³, Masood Ahmad⁴, Raees Ahmad Khan^4,*

Intelligent Automation & Soft Computing, Vol.27, No.2, pp. 409-424, 2021, DOI:10.32604/iasc.2021.015351 - 18 January 2021

Abstract Healthcare devices play an essential role in tracking and managing patient’s safety. However, the complexities of healthcare devices often remain ambiguous due to hardware, software, or the interoperable healthcare system problems. There are essentially two critical factors for targeting healthcare: First, healthcare data is the most valuable entity on the dark web; and the second, it is the easiest to hack. Data pilferage has become a major hazard for healthcare organizations as the hackers now demand ransom and threaten to disclose the sensitive data if not paid within the stipulated timeline. The present study enlists More >

Abdulaziz Attaallah¹, Masood Ahmad², Md Tarique Jamal Ansari², Abhishek Kumar Pandey², Rajeev Kumar^2,3,*, Raees Ahmad Khan²

Intelligent Automation & Soft Computing, Vol.27, No.2, pp. 593-603, 2021, DOI:10.32604/iasc.2021.015092 - 18 January 2021

Abstract Security of the Internet of Healthcare Things (IoHT) devices plays a vital role in e-healthcare today and there has been a rapid increase in the use of networked devices of IoHT in the present healthcare services. However, these networked devices are also highly vulnerable to attackers who constantly target the security of devices and their components to gain access to the patients’ data. Infringement of patients’ data is not only a violation of privacy but can also jeopardize patients’ health if the health records are tampered with. Once the device has been intruded upon, attackers… More >

Muhammad Arslan Usman^1,4,*, Muhammad Rehan Usman², Rizwan Ali Naqvi³, Bernie Mcphilips⁴, Christopher Romeika⁴, Daniel Cunliffe⁴, Christos Politis¹, Nada Philip¹

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 529-547, 2021, DOI:10.32604/cmc.2021.014614 - 12 January 2021

Abstract Video compression in medical video streaming is one of the key technologies associated with mobile healthcare. Seamless delivery of medical video streams over a resource constrained network emphasizes the need of a video codec that requires minimum bitrates and maintains high perceptual quality. This paper presents a comparative study between High Efficiency Video Coding (HEVC) and its potential successor Versatile Video Coding (VVC) in the context of healthcare. A large-scale subjective experiment comprising of twenty-four non-expert participants is presented for eight different test conditions in Full High Definition (FHD) videos. The presented analysis highlights the… More >