Safdar Ali¹, Saad Asad¹, Zeeshan Asghar¹, Atif Ali¹, Dohyeun Kim^2,*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 461-475, 2023, DOI:10.32604/cmc.2023.035672

Abstract The extent of the peril associated with cancer can be perceived from the lack of treatment, ineffective early diagnosis techniques, and most importantly its fatality rate. Globally, cancer is the second leading cause of death and among over a hundred types of cancer; lung cancer is the second most common type of cancer as well as the leading cause of cancer-related deaths. Anyhow, an accurate lung cancer diagnosis in a timely manner can elevate the likelihood of survival by a noticeable margin and medical imaging is a prevalent manner of cancer diagnosis since it is easily accessible to people around… More >

Amel Ali Alhussan^1,*, Hanaa A. Abdallah², Sara Alsodairi², Abdelhamied A. Ateya³

Computer Systems Science and Engineering, Vol.46, No.1, pp. 403-416, 2023, DOI:10.32604/csse.2023.035048

Abstract Securing medical data while transmission on the network is required because it is sensitive and life-dependent data. Many methods are used for protection, such as Steganography, Digital Signature, Cryptography, and Watermarking. This paper introduces a novel robust algorithm that combines discrete wavelet transform (DWT), discrete cosine transform (DCT), and singular value decomposition (SVD) digital image-watermarking algorithms. The host image is decomposed using a two-dimensional DWT (2D-DWT) to approximate low-frequency sub-bands in the embedding process. Then the sub-band low-high (LH) is decomposed using 2D-DWT to four new sub-bands. The resulting sub-band low-high (LH₁) is decomposed using 2D-DWT to four new sub-bands.… More >

M. Muthalakshmi^1,*, T. Merlin Inbamalar², C. Chandravathi³, K. Saravanan⁴

Computer Systems Science and Engineering, Vol.46, No.1, pp. 735-747, 2023, DOI:10.32604/csse.2023.032449

Abstract This research work develops new and better prognostic markers for predicting Childhood MedulloBlastoma (CMB) using a well-defined deep learning architecture. A deep learning architecture could be designed using ideas from image processing and neural networks to predict CMB using histopathological images. First, a convolution process transforms the histopathological image into deep features that uniquely describe it using different two-dimensional filters of various sizes. A 10-layer deep learning architecture is designed to extract deep features. The introduction of pooling layers in the architecture reduces the feature dimension. The extracted and dimension-reduced deep features from the arrangement of convolution layers and pooling… More >

Shixin Tu, Yuanyuan Jia, Jinglong Du^*, Baoru Han^*

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 293-321, 2023, DOI:10.32604/cmes.2023.022308

Abstract The field of healthcare is considered to be the most promising application of intelligent sensor networks. However, the security and privacy protection of medical images collected by intelligent sensor networks is a hot problem that has attracted more and more attention. Fortunately, digital watermarking provides an effective method to solve this problem. In order to improve the robustness of the medical image watermarking scheme, in this paper, we propose a novel zero-watermarking algorithm with the integer wavelet transform (IWT), Schur decomposition and image block energy. Specifically, we first use IWT to extract low-frequency information and divide them into non-overlapping blocks,… More >

Rukiye Karakis^1,2,*

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4649-4666, 2023, DOI:10.32604/cmc.2023.035881

Abstract Medical image steganography aims to increase data security by concealing patient-personal information as well as diagnostic and therapeutic data in the spatial or frequency domain of radiological images. On the other hand, the discipline of image steganalysis generally provides a classification based on whether an image has hidden data or not. Inspired by previous studies on image steganalysis, this study proposes a deep ensemble learning model for medical image steganalysis to detect malicious hidden data in medical images and develop medical image steganography methods aimed at securing personal information. With this purpose in mind, a dataset containing brain Magnetic Resonance… More >

Firas Abedi¹, Subhi R.M. Zeebaree², Zainab Salih Ageed³, Hayder M.A. Ghanimi⁴, Ahmed Alkhayyat^5,*, Mohammed A.M. Sadeeq⁶, Sarmad Nozad Mahmood⁷, Ali S. Abosinnee⁸, Zahraa H. Kareem⁹, Ali Hashim Abbas¹⁰, Waleed Khaild Al-Azzawi¹¹, Mustafa Musa Jaber^12,13, Mohammed Dauwed¹⁴

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5691-5704, 2023, DOI:10.32604/cmc.2023.034435

Abstract As the amount of medical images transmitted over networks and kept on online servers continues to rise, the need to protect those images digitally is becoming increasingly important. However, due to the massive amounts of multimedia and medical pictures being exchanged, low computational complexity techniques have been developed. Most commonly used algorithms offer very little security and require a great deal of communication, all of which add to the high processing costs associated with using them. First, a deep learning classifier is used to classify records according to the degree of concealment they require. Medical images that aren’t needed can… More >

Fuat Türk^*

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1357-1373, 2023, DOI:10.32604/csse.2023.030772

Abstract Covid-19 is a deadly virus that is rapidly spread around the world towards the end of the 2020. The consequences of this virus are quite frightening, especially when accompanied by an underlying disease. The novelty of the virus, the constant emergence of different variants and its rapid spread have a negative impact on the control and treatment process. Although the new test kits provide almost certain results, chest X-rays are extremely important to detect the progression and degree of the disease. In addition to the Covid-19 virus, pneumonia and harmless opacity of the lungs also complicate the diagnosis. Considering the… More >

Mesfer Al Duhayyim^1,*, Mohammed Maray², Ayman Qahmash², Fatma S. Alrayes³, Nuha Alshuqayran⁴, Jaber S. Alzahrani⁵, Mohammed Alghamdi^2,6, Abdullah Mohamed⁷

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3133-3149, 2023, DOI:10.32604/cmc.2023.032740

Abstract Nowadays, security plays an important role in Internet of Things (IoT) environment especially in medical services’ domains like disease prediction and medical data storage. In healthcare sector, huge volumes of data are generated on a daily basis, owing to the involvement of advanced health care devices. In general terms, health care images are highly sensitive to alterations due to which any modifications in its content can result in faulty diagnosis. At the same time, it is also significant to maintain the delicate contents of health care images during reconstruction stage. Therefore, an encryption system is required in order to raise… More >

Walid El-Shafai^1,2, Noha A. El-Hag³, Ahmed Sedik⁴, Ghada Elbanby⁵, Fathi E. Abd El-Samie¹, Naglaa F. Soliman⁶, Hussah Nasser AlEisa^7,*, Mohammed E. Abdel Samea⁸

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2905-2925, 2023, DOI:10.32604/cmc.2023.031936

Abstract Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and therapy. Deep learning provides a high performance for several medical image analysis applications. This paper proposes a deep learning model for the medical image fusion process. This model depends on Convolutional Neural Network (CNN). The basic idea of the proposed model is to extract features from both CT and MR images. Then, an additional process is executed on the extracted features. After that, the fused feature map is reconstructed to obtain the resulting fused image. Finally, the quality of the… More >

Fahad Alraddady¹, E. A. Zanaty², Aida H. Abu bakr³, Walaa M. Abd-Elhafiez^4,5,*

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3627-3646, 2023, DOI:10.32604/cmc.2023.027606

Abstract In this paper, we combine decision fusion methods with four meta-heuristic algorithms (Particle Swarm Optimization (PSO) algorithm, Cuckoo search algorithm, modification of Cuckoo Search (CS McCulloch) algorithm and Genetic algorithm) in order to improve the image segmentation. The proposed technique based on fusing the data from Particle Swarm Optimization (PSO), Cuckoo search, modification of Cuckoo Search (CS McCulloch) and Genetic algorithms are obtained for improving magnetic resonance images (MRIs) segmentation. Four algorithms are used to compute the accuracy of each method while the outputs are passed to fusion methods. In order to obtain parts of the points that determine similar… More >