Mudassir Khalil¹, Muhammad Imran Sharif^2,*, Ahmed Naeem³, Muhammad Umar Chaudhry¹, Hafiz Tayyab Rauf^4,*, Adham E. Ragab⁵

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2031-2047, 2023, DOI:10.32604/cmc.2023.043687

Abstract Early detection of brain tumors is critical for effective treatment planning. Identifying tumors in their nascent stages can significantly enhance the chances of patient survival. While there are various types of brain tumors, each with unique characteristics and treatment protocols, tumors are often minuscule during their initial stages, making manual diagnosis challenging, time-consuming, and potentially ambiguous. Current techniques predominantly used in hospitals involve manual detection via MRI scans, which can be costly, error-prone, and time-intensive. An automated system for detecting brain tumors could be pivotal in identifying the disease in its earliest phases. This research applies several data augmentation techniques… More >

Heba M. El-Hoseny^1,*, Heba F. Elsepae², Wael A. Mohamed², Ayman S. Selmy²

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1855-1872, 2023, DOI:10.32604/cmc.2023.042107

Abstract Diabetic retinopathy is a critical eye condition that, if not treated, can lead to vision loss. Traditional methods of diagnosing and treating the disease are time-consuming and expensive. However, machine learning and deep transfer learning (DTL) techniques have shown promise in medical applications, including detecting, classifying, and segmenting diabetic retinopathy. These advanced techniques offer higher accuracy and performance. Computer-Aided Diagnosis (CAD) is crucial in speeding up classification and providing accurate disease diagnoses. Overall, these technological advancements hold great potential for improving the management of diabetic retinopathy. The study’s objective was to differentiate between different classes of diabetes and verify the… More >

Chia-Wei Jan¹, Yu-Jhih Chiu¹, Kuan-Lin Chen², Ting-Chun Yao³, Ping-Huan Kuo^1,4,*

Computer Systems Science and Engineering, Vol.47, No.3, pp. 2989-3010, 2023, DOI:10.32604/csse.2023.042078

Abstract Pneumonia is a common lung disease that is more prone to affect the elderly and those with weaker respiratory systems. However, hospital medical resources are limited, and sometimes the workload of physicians is too high, which can affect their judgment. Therefore, a good medical assistance system is of great significance for improving the quality of medical care. This study proposed an integrated system by combining transfer learning and gradient-weighted class activation mapping (Grad-CAM). Pneumonia is a common lung disease that is generally diagnosed using X-rays. However, in areas with limited medical resources, a shortage of medical personnel may result in… More >

Nojood O Aljehane^*

Computer Systems Science and Engineering, Vol.47, No.3, pp. 3109-3126, 2023, DOI:10.32604/csse.2023.038042

Abstract Medical image analysis is an active research topic, with thousands of studies published in the past few years. Transfer learning (TL) including convolutional neural networks (CNNs) focused to enhance efficiency on an innovative task using the knowledge of the same tasks learnt in advance. It has played a major role in medical image analysis since it solves the data scarcity issue along with that it saves hardware resources and time. This study develops an Enhanced Tunicate Swarm Optimization with Transfer Learning Enabled Medical Image Analysis System (ETSOTL-MIAS). The goal of the ETSOTL-MIAS technique lies in the identification and classification of… More >

Fayaz Muhammad¹, Jahangir Khan¹, Asad Ullah¹, Fasee Ullah¹, Razaullah Khan², Inayat Khan², Mohammed ElAffendi³, Gauhar Ali^3,*

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 647-664, 2023, DOI:10.32604/cmc.2023.038748

Abstract IIF (Indirect Immune Florescence) has gained much attention recently due to its importance in medical sciences. The primary purpose of this work is to highlight a step-by-step methodology for detecting autoimmune diseases. The use of IIF for detecting autoimmune diseases is widespread in different medical areas. Nearly 80 different types of autoimmune diseases have existed in various body parts. The IIF has been used for image classification in both ways, manually and by using the Computer-Aided Detection (CAD) system. The data scientists conducted various research works using an automatic CAD system with low accuracy. The diseases in the human body… More >

Wenqiu Zhu^1,2, Yongsheng Li^1,2, Qiang Liu^1,2,*, Zhigao Zeng^1,2

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1373-1392, 2023, DOI:10.32604/cmc.2023.037216

Abstract Aiming at the problems of short duration, low intensity, and difficult detection of micro-expressions (MEs), the global and local features of ME video frames are extracted by combining spatial feature extraction and temporal feature extraction. Based on traditional convolution neural network (CNN) and long short-term memory (LSTM), a recognition method combining global identification attention network (GIA), block identification attention network (BIA) and bi-directional long short-term memory (Bi-LSTM) is proposed. In the BIA, the ME video frame will be cropped, and the training will be carried out by cropping into 24 identification blocks (IBs), 10 IBs and uncropped IBs. To alleviate… More >

Muhammad Naeem Akbar^1,*, Seemab Khan², Muhammad Umar Farooq¹, Majed Alhaisoni³, Usman Tariq⁴, Muhammad Usman Akram¹

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3275-3295, 2023, DOI:10.32604/cmc.2023.039289

Abstract The combination of spatiotemporal videos and essential features can improve the performance of human action recognition (HAR); however, the individual type of features usually degrades the performance due to similar actions and complex backgrounds. The deep convolutional neural network has improved performance in recent years for several computer vision applications due to its spatial information. This article proposes a new framework called for video surveillance human action recognition dubbed HybridHR-Net. On a few selected datasets, deep transfer learning is used to pre-trained the EfficientNet-b0 deep learning model. Bayesian optimization is employed for the tuning of hyperparameters of the fine-tuned deep… More >

Muhammad Attique Khan^1,2, Reham R. Mostafa³, Yu-Dong Zhang², Jamel Baili⁴, Majed Alhaisoni⁵, Usman Tariq⁶, Junaid Ali Khan¹, Ye Jin Kim⁷, Jaehyuk Cha^7,*

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3029-3047, 2023, DOI:10.32604/cmc.2023.038838

Abstract Manual diagnosis of brain tumors using magnetic resonance images (MRI) is a hectic process and time-consuming. Also, it always requires an expert person for the diagnosis. Therefore, many computer-controlled methods for diagnosing and classifying brain tumors have been introduced in the literature. This paper proposes a novel multimodal brain tumor classification framework based on two-way deep learning feature extraction and a hybrid feature optimization algorithm. NasNet-Mobile, a pre-trained deep learning model, has been fine-tuned and two-way trained on original and enhanced MRI images. The haze-convolutional neural network (haze-CNN) approach is developed and employed on the original images for contrast enhancement.… More >

Jianying Shi¹, Shengchao Chen¹, Benguo Yu², Yi Ren^3,*, Guanjun Wang^1,4,*, Chenyang Xue⁵

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 3235-3258, 2023, DOI:10.32604/iasc.2023.041392

Abstract Respiratory infections in children increase the risk of fatal lung disease, making effective identification and analysis of breath sounds essential. However, most studies have focused on adults ignoring pediatric patients whose lungs are more vulnerable due to an imperfect immune system, and the scarcity of medical data has limited the development of deep learning methods toward reliability and high classification accuracy. In this work, we collected three types of breath sounds from children with normal (120 recordings), bronchitis (120 recordings), and pneumonia (120 recordings) at the posterior chest position using an off-the-shelf 3M electronic stethoscope. Three features were extracted from… More >

Ying Bai^1,*, Xiaoti Ren², Hong Li¹

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2999-3018, 2023, DOI:10.32604/iasc.2023.040124

Abstract The combination of the Industrial Internet of Things (IIoT) and digital twin (DT) technology makes it possible for the DT model to realize the dynamic perception of equipment status and performance. However, conventional digital modeling is weak in the fusion and adjustment ability between virtual and real information. The performance prediction based on experience greatly reduces the inclusiveness and accuracy of the model. In this paper, a DT-IIoT optimization model is proposed to improve the real-time representation and prediction ability of the key equipment state. Firstly, a global real-time feedback and the dynamic adjustment mechanism is established by combining DT-IIoT… More >