Ping-Huan Kuo^1,2, Sing-Yan Chen¹, Her-Terng Yau^1,2,*

Computer Systems Science and Engineering, Vol.46, No.2, pp. 1577-1596, 2023, DOI:10.32604/csse.2023.036292

Abstract Cars are regarded as an indispensable means of transportation in Taiwan. Several studies have indicated that the automotive industry has witnessed remarkable advances and that the market of used cars has rapidly expanded. In this study, a price prediction system for used BMW cars was developed. Nine parameters of used cars, including their model, registration year, and transmission style, were analyzed. The data obtained were then divided into three subsets. The first subset was used to compare the results of each algorithm. The predicted values produced by the two algorithms with the most satisfactory results were used as the input… More >

José Escorcia-Gutierrez^1,*, Roosvel Soto-Diaz², Natasha Madera³, Carlos Soto³, Francisco Burgos-Florez², Alexander Rodríguez⁴, Romany F. Mansour⁵

Computer Systems Science and Engineering, Vol.46, No.2, pp. 1337-1353, 2023, DOI:10.32604/csse.2023.035253

Abstract Computer-aided diagnosis (CAD) models exploit artificial intelligence (AI) for chest X-ray (CXR) examination to identify the presence of tuberculosis (TB) and can improve the feasibility and performance of CXR for TB screening and triage. At the same time, CXR interpretation is a time-consuming and subjective process. Furthermore, high resemblance among the radiological patterns of TB and other lung diseases can result in misdiagnosis. Therefore, computer-aided diagnosis (CAD) models using machine learning (ML) and deep learning (DL) can be designed for screening TB accurately. With this motivation, this article develops a Water Strider Optimization with Deep Transfer Learning Enabled Tuberculosis Classification… More >

K. Kavin Kumar¹, P. M. Dinesh², P. Rayavel³, L. Vijayaraja⁴, R. Dhanasekar⁴, Rupa Kesavan⁵, Kannadasan Raju⁶, Arfat Ahmad Khan⁷, Chitapong Wechtaisong^8,*, Mohd Anul Haq⁹, Zamil S. Alzamil⁹, Ahmed Alhussen¹⁰

Computer Systems Science and Engineering, Vol.46, No.2, pp. 1845-1861, 2023, DOI:10.32604/csse.2023.033927

Abstract A brain tumor is a lethal neurological disease that affects the average performance of the brain and can be fatal. In India, around 15 million cases are diagnosed yearly. To mitigate the seriousness of the tumor it is essential to diagnose at the beginning. Notwithstanding, the manual evaluation process utilizing Magnetic Resonance Imaging (MRI) causes a few worries, remarkably inefficient and inaccurate brain tumor diagnoses. Similarly, the examination process of brain tumors is intricate as they display high unbalance in nature like shape, size, appearance, and location. Therefore, a precise and expeditious prognosis of brain tumors is essential for implementing… More >

Swagata Boruah¹, Archit Dehloo¹, Prajul Gupta², Manas Ranjan Prusty^3,*, A. Balasundaram³

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 927-942, 2023, DOI:10.32604/cmc.2023.035143

Abstract Diabetic Retinopathy (DR) is a serious hazard that can result in irreversible blindness if not addressed in a timely manner. Hence, numerous techniques have been proposed for the accurate and timely detection of this disease. Out of these, Deep Learning (DL) and Computer Vision (CV) methods for multiclass categorization of color fundus images diagnosed with Diabetic Retinopathy have sparked considerable attention. In this paper, we attempt to develop an extended ResNet152V2 architecture-based Deep Learning model, named ResNet2.0 to aid the timely detection of DR. The APTOS-2019 dataset was used to train the model. This consists of 3662 fundus images belonging… More >

K. Kalyani¹, Sara A Althubiti², Mohammed Altaf Ahmed³, E. Laxmi Lydia⁴, Seifedine Kadry⁵, Neunggyu Han⁶, Yunyoung Nam^6,*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 149-164, 2023, DOI:10.32604/cmc.2023.033005

Abstract Melanoma is a skin disease with high mortality rate while early diagnoses of the disease can increase the survival chances of patients. It is challenging to automatically diagnose melanoma from dermoscopic skin samples. Computer-Aided Diagnostic (CAD) tool saves time and effort in diagnosing melanoma compared to existing medical approaches. In this background, there is a need exists to design an automated classification model for melanoma that can utilize deep and rich feature datasets of an image for disease classification. The current study develops an Intelligent Arithmetic Optimization with Ensemble Deep Transfer Learning Based Melanoma Classification (IAOEDTT-MC) model. The proposed IAOEDTT-MC… More >

Mohammad Yamin¹, Abdullah M. Basahel¹, Mona Abusurrah², Sulafah M Basahel³, Sachi Nandan Mohanty⁴, E. Laxmi Lydia^5,*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 409-425, 2023, DOI:10.32604/cmc.2023.032432

Abstract White blood cells (WBC) or leukocytes are a vital component of the blood which forms the immune system, which is accountable to fight foreign elements. The WBC images can be exposed to different data analysis approaches which categorize different kinds of WBC. Conventionally, laboratory tests are carried out to determine the kind of WBC which is erroneous and time consuming. Recently, deep learning (DL) models can be employed for automated investigation of WBC images in short duration. Therefore, this paper introduces an Aquila Optimizer with Transfer Learning based Automated White Blood Cells Classification (AOTL-WBCC) technique. The presented AOTL-WBCC model executes… More >

Mousa Alhajlah¹, Muhammad Nouman Noor², Muhammad Nazir², Awais Mahmood^1,*, Imran Ashraf³, Tehmina Karamat⁴

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 2227-2245, 2023, DOI:10.32604/cmc.2023.031890

Abstract Gastrointestinal diseases like ulcers, polyps’, and bleeding are increasing rapidly in the world over the last decade. On average 0.7 million cases are reported worldwide every year. The main cause of gastrointestinal diseases is a Helicobacter Pylori (H. Pylori) bacterium that presents in more than 50% of people around the globe. Many researchers have proposed different methods for gastrointestinal disease using computer vision techniques. Few of them focused on the detection process and the rest of them performed classification. The major challenges that they faced are the similarity of infected and healthy regions that misleads the correct classification accuracy. In… More >

Muhammad Zia Ur Rehman¹, Jawad Ahmad^2,*, Emad Sami Jaha³, Abdullah Marish Ali³, Mohammed A. Alzain⁴, Faisal Saeed⁵

Computer Systems Science and Engineering, Vol.46, No.1, pp. 897-911, 2023, DOI:10.32604/csse.2023.035382

Abstract Breast cancer is one of the leading cancers among women. It has the second-highest mortality rate in women after lung cancer. Timely detection, especially in the early stages, can help increase survival rates. However, manual diagnosis of breast cancer is a tedious and time-consuming process, and the accuracy of detection is reliant on the quality of the images and the radiologist’s experience. However, computer-aided medical diagnosis has recently shown promising results, leading to the need to develop an efficient system that can aid radiologists in diagnosing breast cancer in its early stages. The research presented in this paper is focused… More >

Mehwish Shaikh¹, Isma Farah Siddiqui¹, Qasim Arain¹, Jahwan Koo^2,*, Mukhtiar Ali Unar³, Nawab Muhammad Faseeh Qureshi^4,*

Computer Systems Science and Engineering, Vol.46, No.1, pp. 287-302, 2023, DOI:10.32604/csse.2023.035311

Abstract Pneumonia is a dangerous respiratory disease due to which breathing becomes incredibly difficult and painful; thus, catching it early is crucial. Medical physicians’ time is limited in outdoor situations due to many patients; therefore, automated systems can be a rescue. The input images from the X-ray equipment are also highly unpredictable due to variances in radiologists’ experience. Therefore, radiologists require an automated system that can swiftly and accurately detect pneumonic lungs from chest x-rays. In medical classifications, deep convolution neural networks are commonly used. This research aims to use deep pre-trained transfer learning models to accurately categorize CXR images into… More >

Yudong Zhang¹, Muhammad Attique Khan², Ziquan Zhu¹, Shuihua Wang^1,*

Computer Systems Science and Engineering, Vol.46, No.1, pp. 13-26, 2023, DOI:10.32604/csse.2023.034172

Abstract (Aim) The COVID-19 has caused 6.26 million deaths and 522.06 million confirmed cases till 17/May/2022. Chest computed tomography is a precise way to help clinicians diagnose COVID-19 patients. (Method) Two datasets are chosen for this study. The multiple-way data augmentation, including speckle noise, random translation, scaling, salt-and-pepper noise, vertical shear, Gamma correction, rotation, Gaussian noise, and horizontal shear, is harnessed to increase the size of the training set. Then, the SqueezeNet (SN) with complex bypass is used to generate SN features. Finally, the extreme learning machine (ELM) is used to serve as the classifier due to its simplicity of usage,… More >