Technology aided platforms provide reliable tools in almost every field these days. These tools being supported by computational power are significant for applications that need sensitive and precise data analysis. One such important application in the medical field is Automatic Lesion Detection System (ALDS) for skin cancer classification. Computer aided diagnosis helps physicians and dermatologists to obtain a "second opinion" for proper analysis and treatment of skin cancer. Precise segmentation of the cancerous mole along with surrounding area is essential for proper analysis and diagnosis. This paper is focused towards the development of improved ALDS framework based on probabilistic approach that initially utilizes active contours and watershed merged mask for segmenting out the mole and later SVM and Neural Classifier are applied for the classification of the segmented mole. After lesion segmentation, the selected features are classified to ascertain that whether the case under consideration is melanoma or non-melanoma. The approach is tested for varying datasets and comparative analysis is performed that reflects the effectiveness of the proposed system.

Aconitase, the second enzyme of the tricarboxylic acid cycle encoded by ACO1 in the budding yeast Saccharomyces cerevisiae, catalyzes the conversion of citrate to isocitrate. aco1Δ results in mitochondrial DNA (mtDNA) instability. It has been proposed that Aco1 binds to mtDNA and mediates its maintenance. Here we propose an alternative mechanism to account for mtDNA loss in aco1Δ mutant cells. We found that aco1Δ activated the RTG pathway, resulting in increased expression of genes encoding citrate synthase. By deleting RTG1, RTG3, or genes encoding citrate synthase, mtDNA instability was prevented in aco1Δ mutant cells. Increased activity of citrate synthase leads to iron accumulation in the mitochondria. Mutations in MRS3 and MRS4, encoding two mitochondrial iron transporters, also prevented mtDNA loss due to aco1Δ. Mitochondria are the main source of superoxide radicals, which are converted to H2O2 through two superoxide dismutases, Sod1 and Sod2. H2O2 in turn reacts with Fe2+ to generate very active hydroxyl radicals. We found that loss of Sod1, but not Sod2, prevents mtDNA loss in aco1Δ mutant cells. We propose that mtDNA loss in aco1Δ mutant cells is caused by the activation of the RTG pathway and subsequent iron citrate accumulation and toxicity.

AI-based smart thermal perception systems can cater to the limitations of conventional imaging sensors by providing a more reliable data source in low-lighting conditions and adverse weather conditions. This research evaluates and modifies the state-of-the-art object detection and classifier framework for thermal vision with seven key object classes in order to provide superior thermal sensing and scene understanding input for advanced driver-assistance systems (ADAS). The networks are trained on public datasets and is validated on test data with three different test approaches which include test-time augmentation, test-time with no augmentation, and test-time with model ensembling. Additionally, a new model ensemble-based inference engine is proposed, and its efficacy is tested on locally gathered novel test data comprising of 20K thermal frames captured with an uncooled LWIR prototype thermal camera in challenging weather and environmental scenarios. The performance analysis of trained models is investigated by computing precision, recall, and mean average precision scores (mAP). Furthermore, the smaller network variant of thermal-YOLO architecture is optimized using TensorRT inference accelerator, which is then deployed on GPU and resourceconstrained edge hardware Nvidia Jetson Nano. This is implemented to explicitly reduce the inference time on GPU as well as on Nvidia Jetson Nano to evaluate the feasibility for added real-time onboard installations.

Human thermography is considered to be an integral medical diagnostic tool for detecting heat patterns and measuring quantitative temperature data of the human body. It can be used in conjunction with other medical diagnostic procedures for getting comprehensive medication results. In the proposed study we have highlighted the significance of Infrared Thermography (IRT) and the role of machine learning in thermal medical image analysis for human health monitoring and various disease diagnosis in preliminary stages. The first part of the proposed study provides comprehensive information about the application of IRT in the diagnosis of various diseases such as skin and breast cancer detection in preliminary stages, dry eye syndromes, and ocular issues, liver disease, diabetes diagnosis and last but not least the novel COVID-19 virus. Whereas in the second phase we have proposed an autonomous breast tumor classification system using thermal breast images by employing state of the art Convolution Neural Network (CNN). The system achieves the overall accuracy of 80% and recall rate of 83.33%.

This article contains all of the information needed to conduct a study on monocular facial depth estimation problems. A brief literature review and applications on facial depth map research were offered first, followed by a comprehensive evaluation of publicly available facial depth datasets and widely used loss functions. The key properties and characteristics of each facial depth map dataset are described and evaluated. Furthermore, facial depth maps loss functions are briefly discussed, which will make it easier to train neural facial depth models on a variety of datasets for both short-and long-range depth maps. The network's design and components are essential, but its effectiveness is largely determined by how it is trained, which necessitates a large dataset and a suitable loss function. Implementation details of how neural depth networks work and their corresponding evaluation matrices are presented and explained. In addition, an SoA neural model for facial depth estimation is proposed, along with a detailed comparison evaluation and, where feasible, direct comparison of facial depth estimation methods to serve as a foundation for a proposed model that is utilized. The model employed shows better performance compared with current state-of-the-art methods when tested across four datasets. The new loss function used in the proposed method helps the network to learn the facial regions resulting in an accurate depth prediction. The network is trained on synthetic human facial depth datasets whereas for validation purposes real as well as synthetic facial images are used. The results prove that the trained network outperforms current state-of-the-art networks performances, thus setting up a new baseline method for facial depth estimations.