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  • Open Access

    ARTICLE

    Zero Watermarking Algorithm for Medical Image Based on Resnet50-DCT

    Mingshuai Sheng1, Jingbing Li1,2,*, Uzair Aslam Bhatti1,2,3, Jing Liu4, Mengxing Huang1,5, Yen-Wei Chen6

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 293-309, 2023, DOI:10.32604/cmc.2023.036438

    Abstract Medical images are used as a diagnostic tool, so protecting their confidentiality has long been a topic of study. From this, we propose a Resnet50-DCT-based zero watermarking algorithm for use with medical images. To begin, we use Resnet50, a pre-training network, to draw out the deep features of medical images. Then the deep features are transformed by DCT transform and the perceptual hash function is used to generate the feature vector. The original watermark is chaotic scrambled to get the encrypted watermark, and the watermark information is embedded into the original medical image by XOR operation, and the logical key… More >

  • Open Access

    ARTICLE

    Brain Tumor Auto-Segmentation on Multimodal Imaging Modalities Using Deep Neural Network

    Elias Hossain1, Md. Shazzad Hossain2, Md. Selim Hossain3, Sabila Al Jannat4, Moontahina Huda5, Sameer Alsharif6, Osama S. Faragallah7, Mahmoud M. A. Eid8, Ahmed Nabih Zaki Rashed9,*

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4509-4523, 2022, DOI:10.32604/cmc.2022.025977

    Abstract Due to the difficulties of brain tumor segmentation, this paper proposes a strategy for extracting brain tumors from three-dimensional Magnetic Resonance Image (MRI) and Computed Tomography (CT) scans utilizing 3D U-Net Design and ResNet50, taken after by conventional classification strategies. In this inquire, the ResNet50 picked up accuracy with 98.96%, and the 3D U-Net scored 97.99% among the different methods of deep learning. It is to be mentioned that traditional Convolutional Neural Network (CNN) gives 97.90% accuracy on top of the 3D MRI. In expansion, the image fusion approach combines the multimodal images and makes a fused image to extricate… More >

  • Open Access

    ARTICLE

    Transfer Learning on Deep Neural Networks to Detect Pornography

    Saleh Albahli*

    Computer Systems Science and Engineering, Vol.43, No.2, pp. 701-717, 2022, DOI:10.32604/csse.2022.022723

    Abstract While the internet has a lot of positive impact on society, there are negative components. Accessible to everyone through online platforms, pornography is, inducing psychological and health related issues among people of all ages. While a difficult task, detecting pornography can be the important step in determining the porn and adult content in a video. In this paper, an architecture is proposed which yielded high scores for both training and testing. This dataset was produced from 190 videos, yielding more than 19 h of videos. The main sources for the content were from YouTube, movies, torrent, and websites that hosts… More >

  • Open Access

    ARTICLE

    Detection of Diabetic Retinopathy Using Custom CNN to Segment the Lesions

    Saleh Albahli1,2,*, Ghulam Nabi Ahmad Hassan Yar3

    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 837-853, 2022, DOI:10.32604/iasc.2022.024427

    Abstract Diabetic retinopathy is an eye deficiency that affects the retina as a result of the patient having Diabetes Mellitus caused by high sugar levels. This condition causes the blood vessels that nourish the retina to swell and become distorted and eventually become blocked. In recent times, images have played a vital role in using convolutional neural networks to automatically detect medical conditions, retinopathy takes this to another level because there is need not for just a system that could determine is a patient has retinopathy, but also a system that could tell the severity of the procession and if it… More >

  • Open Access

    ARTICLE

    ResNet50-Based Effective Model for Breast Cancer Classification Using Histopathology Images

    Nishant Behar*, Manish Shrivastava

    CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.2, pp. 823-839, 2022, DOI:10.32604/cmes.2022.017030

    Abstract Breast cancer is considered an immense threat and one of the leading causes of mortality in females. It is curable only when detected at an early stage. A standard cancer diagnosis approach involves detection of cancer-related anomalies in tumour histopathology images. Detection depends on the accurate identification of the landmarks in the visual artefacts present in the slide images. Researchers are continuously striving to develop automatic machine-learning algorithms for processing medical images to assist in tumour detection. Nowadays, computer-based automated systems play an important role in cancer image analysis and help healthcare experts make rapid and correct inferences about the… More >

  • Open Access

    ARTICLE

    A Transfer Learning-Based Approach to Detect Cerebral Microbleeds

    Sitara Afzal, Imran Ullah Khan, Jong Weon Lee*

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1903-1923, 2022, DOI:10.32604/cmc.2022.021930

    Abstract Cerebral microbleeds are small chronic vascular diseases that occur because of irregularities in the cerebrum vessels. Individuals and elderly people with brain injury and dementia can have small microbleeds in their brains. A recent study has shown that cerebral microbleeds could be remarkably risky in terms of life and can be riskier for patients with dementia. In this study, we proposed an efficient approach to automatically identify microbleeds by reducing the false positives in openly available susceptibility-weighted imaging (SWI) data samples. The proposed structure comprises two different pre-trained convolutional models with four stages. These stages include (i) skull removal and… More >

  • Open Access

    ARTICLE

    Classification Similarity Network Model for Image Fusion Using Resnet50 and GoogLeNet

    P. Siva Satya Sreedhar1,*, N. Nandhagopal2

    Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1331-1344, 2022, DOI:10.32604/iasc.2022.020918

    Abstract The current trend in Image Fusion (IF) algorithms concentrate on the fusion process alone. However, pay less attention to critical issues such as the similarity between the two input images, features that participate in the Image Fusion. This paper addresses these two issues by deliberately attempting a new Image Fusion framework with Convolutional Neural Network (CNN). CNN has features like pre-training and similarity score, but functionalities are limited. A CNN model with classification prediction and similarity estimation are introduced as Classification Similarity Networks (CSN) to address these issues. ResNet50 and GoogLeNet are modified as the classification branches of CSN v1,… More >

  • Open Access

    ARTICLE

    A Multi-Category Brain Tumor Classification Method Bases on Improved ResNet50

    Linguo Li1,2, Shujing Li1,*, Jian Su3

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2355-2366, 2021, DOI:10.32604/cmc.2021.019409

    Abstract Brain tumor is one of the most common tumors with high mortality. Early detection is of great significance for the treatment and rehabilitation of patients. The single channel convolution layer and pool layer of traditional convolutional neural network (CNN) structure can only accept limited local context information. And most of the current methods only focus on the classification of benign and malignant brain tumors, multi classification of brain tumors is not common. In response to these shortcomings, considering that convolution kernels of different sizes can extract more comprehensive features, we put forward the multi-size convolutional kernel module. And considering that… More >

  • Open Access

    ARTICLE

    Diagnosis of Various Skin Cancer Lesions Based on Fine-Tuned ResNet50 Deep Network

    Sameh Abd ElGhany1,2, Mai Ramadan Ibraheem3, Madallah Alruwaili4, Mohammed Elmogy5,*

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 117-135, 2021, DOI:10.32604/cmc.2021.016102

    Abstract With the massive success of deep networks, there have been significant efforts to analyze cancer diseases, especially skin cancer. For this purpose, this work investigates the capability of deep networks in diagnosing a variety of dermoscopic lesion images. This paper aims to develop and fine-tune a deep learning architecture to diagnose different skin cancer grades based on dermatoscopic images. Fine-tuning is a powerful method to obtain enhanced classification results by the customized pre-trained network. Regularization, batch normalization, and hyperparameter optimization are performed for fine-tuning the proposed deep network. The proposed fine-tuned ResNet50 model successfully classified 7-respective classes of dermoscopic lesions… More >

  • Open Access

    ARTICLE

    A Comprehensive Investigation of Machine Learning Feature Extraction and Classification Methods for Automated Diagnosis of COVID-19 Based on X-ray Images

    Mazin Abed Mohammed1, Karrar Hameed Abdulkareem2, Begonya Garcia-Zapirain3, Salama A. Mostafa4, Mashael S. Maashi5, Alaa S. Al-Waisy1, Mohammed Ahmed Subhi6, Ammar Awad Mutlag7, Dac-Nhuong Le8,9,*

    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 3289-3310, 2021, DOI:10.32604/cmc.2021.012874

    Abstract The quick spread of the Coronavirus Disease (COVID-19) infection around the world considered a real danger for global health. The biological structure and symptoms of COVID-19 are similar to other viral chest maladies, which makes it challenging and a big issue to improve approaches for efficient identification of COVID-19 disease. In this study, an automatic prediction of COVID-19 identification is proposed to automatically discriminate between healthy and COVID-19 infected subjects in X-ray images using two successful moderns are traditional machine learning methods (e.g., artificial neural network (ANN), support vector machine (SVM), linear kernel and radial basis function (RBF), k-nearest neighbor… More >

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