An accurate and prompt diagnosis is essential since lung illnesses are a major global cause of chronic illness and mortality. Conventional techniques for lung disease diagnosis frequently depend on radiologists manually interpreting medical images, which can be laborious and prone to variability in interpretation. Deep learning methods have been demonstrated recently in automating the diagnosis of lung disorders from medical images, especially CT and chest X-rays. This research proposes Luminosity Activated Dense U-Net (LDU-Net) model inspired by U-Net that classifies the lung disease with high accuracy. This research utilizes the 20,000 chest X-Ray images from COVID-19 Radiography Database for lung disease classification. The proposed LDU-Net is built on Deep Neural Network (DNN), in which Three-Layered Dense Block (3-LDB) serves in each layer of the contrasting and expansion block. The LDU-Net model is initiated by masking the original chest X-Ray image with its respective lung segments to form the masked chest X-Ray image. To enhance the lung segmentation, the essential features from masked chest X-Ray images are validated with the luminous intensity through magnitude histogram mapping of image pixels. The masked chest X-Ray images are subjected to form exponential transformed, absolute differenced, contrast and luminous image. The image intensity ratio observed to be high with Luminous Masked Chest X-Ray Vector (LMC) image. To address the data poisoning attack towards security countermeasure, the LMC images are processed to form Watermarked Friendly Poison LMC (WFLMC) image by the proposed Watermarked Friendly Noise Luminosity Activated Dense U-Net (WFLDU-Net) model. The WFLMC images are fitted with 3-LDB in each of three contrasting blocks of the LDU-Net that down samples the feature map by factor 2 and gradually preserves the feature map latent representation by learning the useful features. Each contrasting block is associated with the dropout that alleviates in learning the most essential features from WFLMC images. Then the WFLMC feature maps from the contrasting block are fitted with three expansion blocks that up sample the feature map by factor 2. With the presence of skip connections copy and copy, the decoder block learns the spatial information from the up sampled WFLMC image by replicating the segmentation mask. The novelty of the proposed LDU-Net is attained by integrating the 3-LDB with the proposed U-Net which is again integrated with security countermeasures. The adversarial attempt in the form of data poisoning attack is defended by the proposed WFLDU-Net model with the creation of WFLMC images. The implementation results reveal the proposed LDU-Net model has been found to exhibit high accuracy of 99.36% towards lung disease classification.

In any commercial applications, gender is an important demographic factor that can be utilized to understand the future of retail and the nature of shopping. Nevertheless, a variety of variances in viewing perspectives, facial emotions, extreme instances, background, resolution variations, and face image appearance make gender classification a constant challenging task. This motivates us to propose Canny Edge Feature Vectorized Dense Layered AlexNet (CFVD- AlexNet) inspired by AlexNet which classifies the gender of the face images with high accuracy. The main contribution of this research is three-fold. The initial contribution is through feature extraction using unsupervised learning which is based on the Canny Edge Feature (CEF) extraction technique. The second contribution deals with fine-tuning the AlexNet to design CFVD- AlexNet. The final contribution towards the security countermeasure which overcomes the Dirty Label Backdoor attack method by proposing the CFVD Differentially Private Data Augmentations AlexNet (CFVD DPInstaHide AlexNet). The Face Gender Classification Dataset with 5200 face images is subjected to CEF preprocessing that forms significant Canny Edge Feature Vectorized (CFV) face images. The CFV face images are fitted with the existing CNN models to choose the best CNN model. Experiment results portray that AlexNet and DenseNet offer gender classification with an accuracy above 85% compared to other CNN models. Now, AlexNet has been chosen to add an extra three-layered Dense block after the Fully Connected Layer (FCL) ending with the CFVD- AlexNet model. The implementation results reveal that the proposed CFVD-AlexNet classifies the gender with a high accuracy of 99.32% when compared to the existing models. The Novelty of this research relies on suppressing the Dirty Label Backdoor (DLB) attack by the proposed CFVD DPInstaHide AlexNet model in which the set of security countermeasure of the model are also validated for the successful implementation.

Kidney stone classification is a critical yet complex task in medical imaging, traditionally performed using computed tomography (CT) and ultrasound scans. Manual interpretation of these images is time‐consuming and prone to variability, highlighting the need for automated diagnostic solutions. This study proposes Contour‐Detected Normalized Residual VGG19 (CDR‐VGG19), a deep learning model inspired by VGG19 and enhanced with residual connections to improve classification accuracy. The model leverages contour detection for unsupervised feature extraction, followed by supervised learning using a hybrid of VGG19 and ResNet architectures. Using the Kidney Stone KAGGLE dataset of 2602 images, the model applies data augmentation, preprocessing, and feature filtering. Images are split into training, validation, and testing sets (80:10:10), and multiple CNNs are evaluated. Results show that the proposed CDR‐VGG19 achieves a high classification accuracy of 99.61%, demonstrating its effectiveness in detecting kidney stones from contour‐enhanced images.