Walid El-Shafai^1,2, Hayam A. Abd El-Hameed³, Noha A. El-Hag⁴, Ashraf A. M. Khalaf³, Naglaa F. Soliman⁵, Hussah Nasser AlEisa^6,*, Fathi E. Abd El-Samie¹

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 719-732, 2023, DOI:10.32604/iasc.2023.031079 - 29 September 2022

Abstract Nowadays, the security of images or information is very important. This paper introduces a proposed hybrid watermarking and encryption technique for increasing medical image security. First, the secret medical image is encrypted using Advanced Encryption Standard (AES) algorithm. Then, the secret report of the patient is embedded into the encrypted secret medical image with the Least Significant Bit (LSB) watermarking algorithm. After that, the encrypted secret medical image with the secret report is concealed in a cover medical image, using Kekre’s Median Codebook Generation (KMCG) algorithm. Afterwards, the stego-image obtained is split into 16 parts.… 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 - 22 September 2022

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… More >

Yu Fan^1,6, Jingbing Li^1,2,*, Uzair Aslam Bhatti^1,2, Chunyan Shao¹, Cheng Gong¹, Jieren Cheng^3,5, Yenwei Chen⁴

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1279-1302, 2023, DOI:10.32604/cmc.2023.031445 - 22 September 2022

Abstract Medical images are a critical component of the diagnostic process for clinicians. Although the quality of medical photographs is essential to the accuracy of a physician’s diagnosis, they must be encrypted due to the characteristics of digital storage and information leakage associated with medical images. Traditional watermark embedding algorithm embeds the watermark information into the medical image, which reduces the quality of the medical image and affects the physicians’ judgment of patient diagnosis. In addition, watermarks in this method have weak robustness under high-intensity geometric attacks when the medical image is attacked and the watermarks… More >

Passent El-kafrawy^1,2, Maie Aboghazalah^2,*, Abdelmoty M. Ahmed³, Hanaa Torkey⁴, Ayman El-Sayed⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 909-926, 2023, DOI:10.32604/cmes.2022.021713 - 31 August 2022

Abstract Healthcare systems nowadays depend on IoT sensors for sending data over the internet as a common practice. Encryption of medical images is very important to secure patient information. Encrypting these images consumes a lot of time on edge computing; therefore, the use of an auto-encoder for compression before encoding will solve such a problem. In this paper, we use an auto-encoder to compress a medical image before encryption, and an encryption output (vector) is sent out over the network. On the other hand, a decoder was used to reproduce the original image back after the… More >

Saima Kanwal¹, Feng Tao^1,*, Ahmad Almogren², Ateeq Ur Rehman³, Rizwan Taj¹, Ayman Radwan⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 201-219, 2023, DOI:10.32604/cmes.2022.020255 - 24 August 2022

Abstract The authenticity and integrity of healthcare is the primary objective. Numerous reversible watermarking schemes have been developed to improve the primary objective but increasing the quantity of embedding data leads to covering image distortion and visual quality resulting in data security detection. A trade-off between robustness, imperceptibility, and embedded capacity is difficult to achieve with current algorithms due to limitations in their ability. Keeping this purpose insight, an improved reversibility watermarking methodology is proposed to maximize data embedding capacity and imperceptibility while maintaining data security as a primary concern. A key is generated by a… More >

Prasanalakshmi Balaji^*

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 1-14, 2023, DOI:10.32604/iasc.2023.026341 - 06 June 2022

Abstract Medical image assessment is based on segmentation at its fundamental stage. Deep neural networks have been more popular for segmentation work in recent years. However, the quality of labels has an impact on the training performance of these algorithms, particularly in the medical image domain, where both the interpretation cost and inter-observer variation are considerable. For this reason, a novel optimized deep learning approach is proposed for medical image segmentation. Optimization plays an important role in terms of resources used, accuracy, and the time taken. The noise in the raw medical image are processed using More >

Mesfer Al Duhayyim^1,*, Areej A. Malibari², Marwa Obayya³, Mohamed K. Nour⁴, Ahmed S. Salama⁵, Mohamed I. Eldesouki⁶, Abu Sarwar Zamani⁷, Mohammed Rizwanullah⁷

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5473-5489, 2022, DOI:10.32604/cmc.2022.031976 - 28 July 2022

Abstract The skeletal bone age assessment (BAA) was extremely implemented in development prediction and auxiliary analysis of medicinal issues. X-ray images of hands were detected from the estimation of bone age, whereas the ossification centers of epiphysis and carpal bones are important regions. The typical skeletal BAA approaches remove these regions for predicting the bone age, however, few of them attain suitable efficacy or accuracy. Automatic BAA techniques with deep learning (DL) methods are reached the leading efficiency on manual and typical approaches. Therefore, this study introduces an intellectual skeletal bone age assessment and classification with… More >

C. S. S. Anupama¹, Raed Alsini², N. Supriya³, E. Laxmi Lydia⁴, Seifedine Kadry⁵, Sang-Soo Yeo⁶, Yongsung Kim^7,*

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3219-3233, 2022, DOI:10.32604/cmc.2022.030267 - 16 June 2022

Abstract Internet of Things (IoT) and blockchain receive significant interest owing to their applicability in different application areas such as healthcare, finance, transportation, etc. Medical image security and privacy become a critical part of the healthcare sector where digital images and related patient details are communicated over the public networks. This paper presents a new wind driven optimization algorithm based medical image encryption (WDOA-MIE) technique for blockchain enabled IoT environments. The WDOA-MIE model involves three major processes namely data collection, image encryption, optimal key generation, and data transmission. Initially, the medical images were captured from the… More >

Mohammad Alamgeer¹, Hanan Abdullah Mengash², Radwa Marzouk², Mohamed K Nour³, Anwer Mustafa Hilal^4,*, Abdelwahed Motwakel⁴, Abu Sarwar Zamani⁴, Mohammed Rizwanullah⁴

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1437-1448, 2022, DOI:10.32604/cmc.2022.027896 - 18 May 2022

Abstract Early detection of lung cancer can help for improving the survival rate of the patients. Biomedical imaging tools such as computed tomography (CT) image was utilized to the proper identification and positioning of lung cancer. The recently developed deep learning (DL) models can be employed for the effectual identification and classification of diseases. This article introduces novel deep learning enabled CAD technique for lung cancer using biomedical CT image, named DLCADLC-BCT technique. The proposed DLCADLC-BCT technique intends for detecting and classifying lung cancer using CT images. The proposed DLCADLC-BCT technique initially uses gray level co-occurrence More >

Bassam A. Y. Alqaralleh^1,*, Fahad Aldhaban¹, Anas Abukaraki², Esam A. AlQaralleh³

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 87-101, 2022, DOI:10.32604/cmc.2022.026729 - 18 May 2022

Abstract Diabetic Retinopathy (DR) has become a widespread illness among diabetics across the globe. Retinal fundus images are generally used by physicians to detect and classify the stages of DR. Since manual examination of DR images is a time-consuming process with the risks of biased results, automated tools using Artificial Intelligence (AI) to diagnose the disease have become essential. In this view, the current study develops an Optimal Deep Learning-enabled Fusion-based Diabetic Retinopathy Detection and Classification (ODL-FDRDC) technique. The intention of the proposed ODL-FDRDC technique is to identify DR and categorize its different grades using retinal More >