Jun Liu¹, Geng Yuan², Changdi Yang², Houbing Song³, Liang Luo^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.2, pp. 1571-1587, 2023, DOI:10.32604/cmes.2022.023195

Abstract The interpretability of deep learning models has emerged as a compelling area in artificial intelligence research. The safety criteria for medical imaging are highly stringent, and models are required for an explanation. However, existing convolutional neural network solutions for left ventricular segmentation are viewed in terms of inputs and outputs. Thus, the interpretability of CNNs has come into the spotlight. Since medical imaging data are limited, many methods to fine-tune medical imaging models that are popular in transfer models have been built using massive public ImageNet datasets by the transfer learning method. Unfortunately, this generates many unreliable parameters and makes… More >

R. Jayaraj^1,*, S. Lokesh²

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 481-497, 2023, DOI:10.32604/iasc.2023.030495

Abstract Every day, websites and personal archives create more and more photos. The size of these archives is immeasurable. The comfort of use of these huge digital image gatherings donates to their admiration. However, not all of these folders deliver relevant indexing information. From the outcomes, it is difficult to discover data that the user can be absorbed in. Therefore, in order to determine the significance of the data, it is important to identify the contents in an informative manner. Image annotation can be one of the greatest problematic domains in multimedia research and computer vision. Hence, in this paper, Adaptive… More >

Shakunthala Masi^*, Helenprabha Kuttiappan

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 733-744, 2023, DOI:10.32604/iasc.2023.025919

Abstract In medical science for envisaging human body’s phenomenal structure a major part has been driven by image processing techniques. Major objective of this work is to detect of cerebral atherosclerosis for image segmentation application. Detection of some abnormal structures in human body has become a difficult task to complete with some simple images. For expounding and distinguishing neural architecture of human brain in an effective manner, MRI (Magnetic Resonance Imaging) is one of the most suitable and significant technique. Here we work on detection of Cerebral Atherosclerosis from MRI images of patients. Cerebral Atherosclerosis is a cerebral vascular disease causes… More >

R. Poonguzhali¹, Sultan Ahmad², P. Thiruvannamalai Sivasankar³, S. Anantha Babu³, Pranav Joshi⁴, Gyanendra Prasad Joshi⁵, Sung Won Kim^6,*

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2179-2194, 2023, DOI:10.32604/cmc.2023.032816

Abstract Automated segmentation and classification of biomedical images act as a vital part of the diagnosis of brain tumors (BT). A primary tumor brain analysis suggests a quicker response from treatment that utilizes for improving patient survival rate. The location and classification of BTs from huge medicinal images database, obtained from routine medical tasks with manual processes are a higher cost together in effort and time. An automatic recognition, place, and classifier process was desired and useful. This study introduces an Automated Deep Residual U-Net Segmentation with Classification model (ADRU-SCM) for Brain Tumor Diagnosis. The presented ADRU-SCM model majorly focuses on… More >

Mazen Mushabab Alqahtani¹, Ashit Kumar Dutta², Sultan Almotairi³, M. Ilayaraja⁴, Amani Abdulrahman Albraikan⁵, Fahd N. Al-Wesabi^6,7,*, Mesfer Al Duhayyim⁸

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 217-233, 2023, DOI:10.32604/cmc.2023.025280

Abstract Recent developments in digital cameras and electronic gadgets coupled with Machine Learning (ML) and Deep Learning (DL)-based automated apple leaf disease detection models are commonly employed as reasonable alternatives to traditional visual inspection models. In this background, the current paper devises an Effective Sailfish Optimizer with EfficientNet-based Apple Leaf disease detection (ESFO-EALD) model. The goal of the proposed ESFO-EALD technique is to identify the occurrence of plant leaf diseases automatically. In this scenario, Median Filtering (MF) approach is utilized to boost the quality of apple plant leaf images. Moreover, SFO with Kapur's entropy-based segmentation technique is also utilized for the… More >

Anjani Kumar Rai^*, Subodh Srivastava

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.3, pp. 1489-1528, 2023, DOI:10.32604/cmes.2022.020920

Abstract Image forging is the alteration of a digital image to conceal some of the necessary or helpful information. It cannot be easy to distinguish the modified region from the original image in some circumstances. The demand for authenticity and the integrity of the image drive the detection of a fabricated image. There have been cases of ownership infringements or fraudulent actions by counterfeiting multimedia files, including re-sampling or copy-moving. This work presents a high-level view of the forensics of digital images and their possible detection approaches. This work presents a thorough analysis of digital image forgery detection techniques with their… More >

Xindai An¹, Di Wu^1,2,*, Xiangwen Xie¹, Kefeng Song¹

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1091-1103, 2023, DOI:10.32604/cmes.2022.020670

Abstract So far, slope collapse detection mainly depends on manpower, which has the following drawbacks: (1) low reliability, (2) high risk of human safe, (3) high labor cost. To improve the efficiency and reduce the human investment of slope collapse detection, this paper proposes an intelligent detection method based on deep learning technology for the task. In this method, we first use the deep learning-based image segmentation technology to find the slope area from the captured scene image. Then the foreground motion detection method is used for detecting the motion of the slope area. Finally, we design a lightweight convolutional neural… More >

Wenbin Wu¹, Guanjun Liu^1,*, Kaiyi Liang², Hui Zhou²

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1323-1335, 2023, DOI:10.32604/cmes.2022.020428

Abstract Deep neural networks are now widely used in the medical image segmentation field for their performance superiority and no need of manual feature extraction. U-Net has been the baseline model since the very beginning due to a symmetrical U-structure for better feature extraction and fusing and suitable for small datasets. To enhance the segmentation performance of U-Net, cascaded U-Net proposes to put two U-Nets successively to segment targets from coarse to fine. However, the plain cascaded U-Net faces the problem of too less between connections so the contextual information learned by the former U-Net cannot be fully used by the… More >

Mahmoud Ragab^1,2,3,*, Jaber Alyami^4,5

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2309-2322, 2023, DOI:10.32604/csse.2023.026877

Abstract Liver cancer is one of the major diseases with increased mortality in recent years, across the globe. Manual detection of liver cancer is a tedious and laborious task due to which Computer Aided Diagnosis (CAD) models have been developed to detect the presence of liver cancer accurately and classify its stages. Besides, liver cancer segmentation outcome, using medical images, is employed in the assessment of tumor volume, further treatment plans, and response monitoring. Hence, there is a need exists to develop automated tools for liver cancer detection in a precise manner. With this motivation, the current study introduces an Intelligent… More >

V. Thanikachalam^1,*, M. G. Kavitha², V. Sivamurugan¹

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2129-2145, 2023, DOI:10.32604/csse.2023.026527

Abstract In recent times, Internet of Things (IoT) and Deep Learning (DL) models have revolutionized the diagnostic procedures of Diabetic Retinopathy (DR) in its early stages that can save the patient from vision loss. At the same time, the recent advancements made in Machine Learning (ML) and DL models help in developing Computer Aided Diagnosis (CAD) models for DR recognition and grading. In this background, the current research works designs and develops an IoT-enabled Effective Neutrosophic based Segmentation with Optimal Deep Belief Network (ODBN) model i.e., NS-ODBN model for diagnosis of DR. The presented model involves Interval Neutrosophic Set (INS) technique… More >