Sesikala Bapatla¹, J. Harikiran^2,*

Computer Systems Science and Engineering, Vol.46, No.1, pp. 597-617, 2023, DOI:10.32604/csse.2023.034998

Abstract Diabetes problems can lead to an eye disease called Diabetic Retinopathy (DR), which permanently damages the blood vessels in the retina. If not treated early, DR becomes a significant reason for blindness. To identify the DR and determine the stages, medical tests are very labor-intensive, expensive, and time-consuming. To address the issue, a hybrid deep and machine learning technique-based autonomous diagnostic system is provided in this paper. Our proposal is based on lesion segmentation of the fundus images based on the LuNet network. Then a Refined Attention Pyramid Network (RAPNet) is used for extracting global and local features. To increase… More >

Mudiyala Aparna, Battula Srinivasa Rao^*

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6909-6932, 2023, DOI:10.32604/cmc.2023.034796

Abstract Neurological disorders such as Alzheimer’s disease (AD) are very challenging to treat due to their sensitivity, technical challenges during surgery, and high expenses. The complexity of the brain structures makes it difficult to distinguish between the various brain tissues and categorize AD using conventional classification methods. Furthermore, conventional approaches take a lot of time and might not always be precise. Hence, a suitable classification framework with brain imaging may produce more accurate findings for early diagnosis of AD. Therefore in this paper, an effective hybrid Xception and Fractalnet-based deep learning framework are implemented to classify the stages of AD into… More >

Sureshkumar Rajagopal¹, Tamilvizhi Thanarajan^2,*, Youseef Alotaibi³, Saleh Alghamdi⁴

Computer Systems Science and Engineering, Vol.45, No.2, pp. 2093-2109, 2023, DOI:10.32604/csse.2023.032488

Abstract Automated segmentation of brain tumors using Magnetic Resonance Imaging (MRI) data is critical in the analysis and monitoring of disease development. As a result, gliomas are aggressive and diverse tumors that may be split into intra-tumoral groups by using effective and accurate segmentation methods. It is intended to extract characteristics from an image using the Gray Level Co-occurrence (GLC) matrix feature extraction method described in the proposed work. Using Convolutional Neural Networks (CNNs), which are commonly used in biomedical image segmentation, CNNs have significantly improved the precision of the state-of-the-art segmentation of a brain tumor. Using two segmentation networks, a… More >

Tianyu Liu^1,3,4, Pengyu Liu^1,2,3,4,*, Xiaowei Jia⁵, Shanji Chen², Ying Ma², Qian Gao^1,3,4

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1033-1045, 2023, DOI:10.32604/csse.2023.028225

Abstract Image segmentation of sea-land remote sensing images is of great importance for downstream applications including shoreline extraction, the monitoring of near-shore marine environment, and near-shore target recognition. To mitigate large number of parameters and improve the segmentation accuracy, we propose a new Squeeze-Depth-Wise UNet (SDW-UNet) deep learning model for sea-land remote sensing image segmentation. The proposed SDW-UNet model leverages the squeeze-excitation and depth-wise separable convolution to construct new convolution modules, which enhance the model capacity in combining multiple channels and reduces the model parameters. We further explore the effect of position-encoded information in NLP (Natural Language Processing) domain on sea-land… More >

S. Gnanapriya^1,*, K. Rahimunnisa²

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 1105-1119, 2023, DOI:10.32604/iasc.2023.032832

Abstract The performance of Hand Gesture Recognition (HGR) depends on the hand shape. Segmentation helps in the recognition of hand gestures for more accuracy and improves the overall performance compared to other existing deep neural networks. The crucial segmentation task is extremely complicated because of the background complexity, variation in illumination etc. The proposed modified UNET and ensemble model of Convolutional Neural Networks (CNN) undergoes a two stage process and results in proper hand gesture recognition. The first stage is segmenting the regions of the hand and the second stage is gesture identification. The modified UNET segmentation model is trained using… More >

A. Deepak Kumar^1,2,*, T. Sasipraba¹

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2273-2288, 2023, DOI:10.32604/iasc.2023.028996

Abstract Diabetic Retinopathy is a disease, which happens due to abnormal growth of blood vessels that causes spots on the vision and vision loss. Various techniques are applied to identify the disease in the early stage with different methods and parameters. Machine Learning (ML) techniques are used for analyzing the images and finding out the location of the disease. The restriction of the ML is a dataset size, which is used for model evaluation. This problem has been overcome by using an augmentation method by generating larger datasets with multidimensional features. Existing models are using only one augmentation technique, which produces… More >

S. Jeevitha^1,*, K. Valarmathi²

Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1123-1139, 2022, DOI:10.32604/iasc.2022.024746

Abstract Pulmonary Embolism is a blood clot in the lung which restricts the blood flow and reduces blood oxygen level resulting in mortality if it is untreated. Further, pulmonary embolism is evidenced prominently in the segmental and sub-segmental regions of the computed tomography angiography images in COVID-19 patients. Pulmonary embolism detection from these images is a significant research problem in the challenging COVID-19 pandemic in the venture of early disease detection, treatment, and prognosis. Inspired by several investigations based on deep learning in this context, a two-stage framework has been proposed for pulmonary embolism detection which is realized as a segmentation… More >

Mimouna Abdullah Alkhonaini¹, Siwar Ben Haj Hassine², Marwa Obayya³, Fahd N. Al-Wesabi², Anwer Mustafa Hilal^4,*, Manar Ahmed Hamza⁴, Abdelwahed Motwakel⁴, Mesfer Al Duhayyim⁵

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3511-3527, 2022, DOI:10.32604/cmc.2022.024583

Abstract The unstructured growth of abnormal cells in the lung tissue creates tumor. The early detection of lung tumor helps the patients avoiding the death rate and gives better treatment. Various medical image modalities can help the physicians in the diagnosis of disease. Many research works have been proposed for the early detection of lung tumor. High computation time and misidentification of tumor are the prevailing issues. In order to overcome these issues, this paper has proposed a hybrid classifier of Atrous Spatial Pyramid Pooling (ASPP)-Unet architecture with Whale Optimization Algorithm (ASPP-Unet -WOA). To get a fine tuning detection of tumor… More >

Batyrkhan Omarov^1,2,3, Azhar Tursynova^1,*, Octavian Postolache⁴, Khaled Gamry⁵, Aidar Batyrbekov⁵, Sapargali Aldeshov^6,7, Zhanar Azhibekova⁹, Marat Nurtas^5,8, Akbayan Aliyeva⁶, Kadrzhan Shiyapov^10,11

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4701-4717, 2022, DOI:10.32604/cmc.2022.020998

Abstract The task of segmentation of brain regions affected by ischemic stroke is help to tackle important challenges of modern stroke imaging analysis. Unfortunately, at the moment, the models for solving this problem using machine learning methods are far from ideal. In this paper, we consider a modified 3D UNet architecture to improve the quality of stroke segmentation based on 3D computed tomography images. We use the ISLES 2018 (Ischemic Stroke Lesion Segmentation Challenge 2018) open dataset to train and test the proposed model. Interpretation of the obtained results, as well as the ideas for further experiments are included in the… More >

A. Devipriya¹, P. Prabu², K. Venkatachalam³, Ahmed Zohair Ibrahim^4,*

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 651-666, 2022, DOI:10.32604/cmc.2022.020820

Abstract This research article proposes an automatic frame work for detecting COVID -19 at the early stage using chest X-ray image. It is an undeniable fact that coronovirus is a serious disease but the early detection of the virus present in human bodies can save lives. In recent times, there are so many research solutions that have been presented for early detection, but there is still a lack in need of right and even rich technology for its early detection. The proposed deep learning model analysis the pixels of every image and adjudges the presence of virus. The classifier is designed… More >