Medical imaging is a field of medicine where doctors use images of different body organs to treat or diagnose patients. Nowadays, medical image segmentation, compression, and security are currently relatively difficult issues for illness diagnosis. These medical pictures are being sent via the internet; thus data must be protected against cyberattacks. Medical images are extremely sensitive to even slight changes, and data volumes are dramatically increasing the amount of the data. To protect the confidentiality of digital images saved online, privacy and security must be ensured.  In this paper, a novel DL-based Generative Adversarial Network (GAN) with tent map and hash-map utilized to generate a robust private key. The fake image is generated by using GAN. T It is suggested to use the 2D-Henon Sine Map (2D-HSM), DNA computing, chaotic maps, and a SHA-512-based strategy are proposed. The SHA-512 algorithm and the 2D-HSM are used to construct the key. The Henon map and the Mersenne Twister are used in a two-level encryption method that is shown (MT). After that, a DNA computing-based XOR operation is performed using the key. A decoding procedure based on DNA rules captures the encoded images. The comprehensive outcome is based on several security measures, such as key space, SSIM, information entropy, PSNR, and histogram analysis. The proposed technique performs better than the existing approaches.

Medical images are utilized to diagnose patients' health conditions. Nowadays, medical images are sent over the internet for diagnosis purposes. So, they should be protected from cyber attackers. These medical images are sensitive to any minor changes, and the data volume is rapidly increasing. Thus, security and storage costs must be considered in medical images. Traditional encryption and compression methods are ineffective for encrypting medical images due to their high execution time and algorithm complexity. In this paper, a novel 2D-chaos and Generative Adversarial Network (GAN) with DeoxyriboNucleic Acid (DNA) computing is proposed for generating encryption keys and improving the security of medical images. The proposed scheme uses GAN and 2D-chaos to generate the private key and diffusion process. The pixel values of the original images in the proposed schemes are shuffled using Mersenne Twister (MT) to improve the security of medical images. Moreover, the novel 2D-Chaotic Tent Map (2D-CTM) method is used to construct the key while performing XOR-based encryption. The proposed model has been tested on different medical images, namely the COVIDx-19 X-ray images, the malaria microscopic images, and the brain MRI images. The experiment results have been evaluated using performance metrics, namely key space, histogram analysis, entropy, key sensitivity, robustness analysis, correlation, SSIM, and MS-SSIM. The outcomes demonstrate that the proposed scheme is more effective than the state-of-the-art schemes.

Colorectal cancers are the third-largest kind of cancer in the world. However, detecting and removing precursor polyps with adenomatous cells using optical colonoscopy images helps to prevent this type of cancer. Moreover, hyperplastic polyps are benign cancers; adenomatous polyps are more likely to grow into cancerous tumors. Therefore, the detection and segmentation of polyps provide further histological evaluation. However, the main challenge is the extensive range of infected polyp features inside the colon and the lack of contrast between normal and infected areas. To solve these issues, the proposed novel Customized ResNet50 with DeepLabV3Plus Network (CRDL-PNet) model provided a scheme for segmenting polyps from colonoscopy images. The customized ResNet50 extracted features from polyp colonoscopy images. Furthermore, Atrous Spatial Pyramid Pooling (ASPP) is used to handle scale variation during training and improve feature selection maps in an upsampling layer. Additionally, the Gateaux Derivatives (GD) approach is used to segment boundary boxes of polyp regions. The proposed method has been evaluated on four datasets, namely the Kvasir-SEG, ETIS-PolypLaribDB, CVC-ClinicDB, and CVC-ColonDB datasets, for segmenting and detecting polyps. The simulation results have been examined by evaluation metrics, such as accuracy, Intersection-Over-Union (IOU), mean IOU, precision, recall, F1-score, dice, Jaccard, and Mean Process Time per Frame (MPTF) for proper validation. The proposed scheme outperforms the existing State-Of-The-Arts (SOTA) model on the same polyp datasets.