Anas AbuKaraki¹, Tawfi Alrawashdeh¹, Sumaya Abusaleh¹, Malek Zakarya Alksasbeh^1,*, Bilal Alqudah¹, Khalid Alemerien², Hamzah Alshamaseen³

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1055-1073, 2024, DOI:10.32604/cmc.2024.051420

Abstract This paper presents a novel multiclass system designed to detect pleural effusion and pulmonary edema on chest X-ray images, addressing the critical need for early detection in healthcare. A new comprehensive dataset was formed by combining 28,309 samples from the ChestX-ray14, PadChest, and CheXpert databases, with 10,287, 6022, and 12,000 samples representing Pleural Effusion, Pulmonary Edema, and Normal cases, respectively. Consequently, the preprocessing step involves applying the Contrast Limited Adaptive Histogram Equalization (CLAHE) method to boost the local contrast of the X-ray samples, then resizing the images to 380 × 380 dimensions, followed by using the data… More >

Sujithra Sankar^1,*, S. Sathyalakshmi²

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3111-3138, 2024, DOI:10.32604/cmc.2024.048408

Abstract In the era of advanced machine learning techniques, the development of accurate predictive models for complex medical conditions, such as thyroid cancer, has shown remarkable progress. Accurate predictive models for thyroid cancer enhance early detection, improve resource allocation, and reduce overtreatment. However, the widespread adoption of these models in clinical practice demands predictive performance along with interpretability and transparency. This paper proposes a novel association-rule based feature-integrated machine learning model which shows better classification and prediction accuracy than present state-of-the-art models. Our study also focuses on the application of SHapley Additive exPlanations (SHAP) values as… More >

Muhammad Hammad Waseem¹, Malik Sajjad Ahmed Nadeem^1,*, Ishtiaq Rasool Khan², Seong-O-Shim³, Wajid Aziz¹, Usman Habib⁴

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4293-4315, 2023, DOI:10.32604/cmc.2023.036710

Abstract Machine Learning (ML)-based prediction and classification systems employ data and learning algorithms to forecast target values. However, improving predictive accuracy is a crucial step for informed decision-making. In the healthcare domain, data are available in the form of genetic profiles and clinical characteristics to build prediction models for complex tasks like cancer detection or diagnosis. Among ML algorithms, Artificial Neural Networks (ANNs) are considered the most suitable framework for many classification tasks. The network weights and the activation functions are the two crucial elements in the learning process of an ANN. These weights affect the… More >

Emad Abd Al Rahman¹, Nur Intan Raihana Ruhaiyem^1,*, Majed Bouchahma², Kamarul Imran Musa³

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3007-3028, 2023, DOI:10.32604/iasc.2023.032580

Abstract This study offers a framework for a breast cancer computer-aided treatment prediction (CATP) system. The rising death rate among women due to breast cancer is a worldwide health concern that can only be addressed by early diagnosis and frequent screening. Mammography has been the most utilized breast imaging technique to date. Radiologists have begun to use computer-aided detection and diagnosis (CAD) systems to improve the accuracy of breast cancer diagnosis by minimizing human errors. Despite the progress of artificial intelligence (AI) in the medical field, this study indicates that systems that can anticipate a treatment… More >

Neenavath Veeraiah^1,*, Youseef Alotaibi², Ahmad F. Subahi³

Computer Systems Science and Engineering, Vol.46, No.2, pp. 1879-1900, 2023, DOI:10.32604/csse.2023.034658

Abstract An abnormality that develops in white blood cells is called leukemia. The diagnosis of leukemia is made possible by microscopic investigation of the smear in the periphery. Prior training is necessary to complete the morphological examination of the blood smear for leukemia diagnosis. This paper proposes a Histogram Threshold Segmentation Classifier (HTsC) for a decision support system. The proposed HTsC is evaluated based on the color and brightness variation in the dataset of blood smear images. Arithmetic operations are used to crop the nucleus based on automated approximation. White Blood Cell (WBC) segmentation is calculated… More >

Hiam Alquran^1,2, Yazan Al-Issa³, Mohammed Alslatie⁴, Isam Abu-Qasmieh¹, Amin Alqudah³, Wan Azani Mustafa^5,7,*, Yasmin Mohd Yacob^6,7

Computer Systems Science and Engineering, Vol.46, No.2, pp. 1653-1671, 2023, DOI:10.32604/csse.2023.033861

Abstract Liver cancer is the second leading cause of cancer death worldwide. Early tumor detection may help identify suitable treatment and increase the survival rate. Medical imaging is a non-invasive tool that can help uncover abnormalities in human organs. Magnetic Resonance Imaging (MRI), in particular, uses magnetic fields and radio waves to differentiate internal human organs tissue. However, the interpretation of medical images requires the subjective expertise of a radiologist and oncologist. Thus, building an automated diagnosis computer-based system can help specialists reduce incorrect diagnoses. This paper proposes a hybrid automated system to compare the performance… More >

Hong-Hu Chu¹, Muhammad Rizwan Saeed², Javed Rashid^3,4,*, Muhammad Tahir Mehmood⁵, Israr Ahmad⁶, Rao Sohail Iqbal⁴, Ghulam Ali¹

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1863-1881, 2023, DOI:10.32604/cmc.2023.035287

Abstract The increasing global population at a rapid pace makes road traffic dense; managing such massive traffic is challenging. In developing countries like Pakistan, road traffic accidents (RTA) have the highest mortality percentage among other Asian countries. The main reasons for RTAs are road cracks and potholes. Understanding the need for an automated system for the detection of cracks and potholes, this study proposes a decision support system (DSS) for an autonomous road information system for smart city development with the use of deep learning. The proposed DSS works in layers where initially the image of… More >

Ahmed A. Alsheikhy^1,*, Yahia F. Said¹, Tawfeeq Shawly²

Computer Systems Science and Engineering, Vol.46, No.1, pp. 799-817, 2023, DOI:10.32604/csse.2023.035269

Abstract Lung cancer is the leading cause of cancer-related death around the globe. The treatment and survival rates among lung cancer patients are significantly impacted by early diagnosis. Most diagnostic techniques can identify and classify only one type of lung cancer. It is crucial to close this gap with a system that detects all lung cancer types. This paper proposes an intelligent decision support system for this purpose. This system aims to support the quick and early detection and classification of all lung cancer types and subtypes to improve treatment and save lives. Its algorithm uses… More >

B. Karthikeyan^1,*, K. Nithya², Ahmed Alkhayyat³, Yousif Kerrar Yousif⁴

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2299-2313, 2023, DOI:10.32604/iasc.2023.032585

Abstract In today’s digital era, e-healthcare systems exploit digital technologies and telecommunication devices such as mobile devices, computers and the internet to provide high-quality healthcare services. E-healthcare decision support systems have been developed to optimize the healthcare services and enhance a patient’s health. These systems enable rapid access to the specialized healthcare services via reliable information, retrieved from the cases or the patient histories. This phenomenon reduces the time taken by the patients to physically visit the healthcare institutions. In the current research work, a new Shuffled Frog Leap Optimizer with Deep Learning-based Decision Support System… More >

Hanan Abdullah Mengash¹, Lubna A. Alharbi², Saud S. Alotaibi³, Sarab AlMuhaideb⁴, Nadhem Nemri⁵, Mrim M. Alnfiai⁶, Radwa Marzouk¹, Ahmed S. Salama⁷, Mesfer Al Duhayyim^8,*

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4483-4500, 2023, DOI:10.32604/cmc.2023.032588

Abstract In recent times, cities are getting smart and can be managed effectively through diverse architectures and services. Smart cities have the ability to support smart medical systems that can infiltrate distinct events (i.e., smart hospitals, smart homes, and community health centres) and scenarios (e.g., rehabilitation, abnormal behavior monitoring, clinical decision-making, disease prevention and diagnosis postmarking surveillance and prescription recommendation). The integration of Artificial Intelligence (AI) with recent technologies, for instance medical screening gadgets, are significant enough to deliver maximum performance and improved management services to handle chronic diseases. With latest developments in digital data collection,… More >