Computer based diagnosis of Alzheimer's disease can be performed by dint of the analysis of the functional and structural changes in the brain. Multispectral image fusion deliberates upon fusion of the complementary information while discarding the surplus information to achieve a solitary image which encloses both spatial and spectral details. This paper presents a Non-Sub-sampled Contourlet Transform (NSCT) based multispectral image fusion model for computer-aided diagnosis of Alzheimer's disease. The proposed fusion methodology involves color transformation of the input multispectral image. The multispectral image in YIQ color space is decomposed using NSCT followed by dimensionality reduction using modified Principal Component Analysis algorithm on the low frequency coefficients. Further, the high frequency coefficients are enhanced using non-linear enhancement function. Two different fusion rules are then applied to the low-pass and high-pass sub-bands: Phase congruency is applied to low frequency coefficients and a combination of directive contrast and normalized Shannon entropy is applied to high frequency coefficients. The superiority of the fusion response is depicted by the comparisons made with the other state-of-the-art fusion approaches (in terms of various fusion metrics).

Object Tracking (OT) on a Moving Camera socalled Moving Object Tracking (MOT) is extremely vital in Computer Vision. While other conventional tracking methods based on fixed camera can only track the objects in its range, a moving camera can tackle this issue by following the objects. Moreover, single tracker is used widely to track object but it is not effective due to the moving camera because the challenges such as sudden movements, blurring, pose variation. The paper proposes a method inherited by tracking by detection approach. It integrates a single tracker with object detection method. The proposed tracking system can track object efficiency and effectively because object detection method can be used to find the tracked object again if the single tracker loses track. Three main contributions are presented in the paper as follow. First, the proposed Unified Visual based-MOT system can do the tasks such as Localization, 3D Environment Reconstruction and Tracking based on Stereo Camera and Inertial Measurement Unit (IMU). Second, it takes into account camera motion and the moving objects to improve the precision rate in localization and tracking. Third, proposed tracking system based on integration of single tracker as Deep Particle Filter and Object Detection as Yolov3. The overall system is tested on the dataset KITTI 2012, and it has achieved a good accuracy rate in real time.

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With the rapid growth of the autonomous system, deep learning has become integral parts to enumerate applications especially in the case of healthcare systems. Human body vertebrae are the longest and complex parts of the human body. There are numerous kinds of conditions such as scoliosis, vertebra degeneration, and vertebrate disc spacing that are related to the human body vertebrae or spine or backbone. Early detection of these problems is very important otherwise patients will suffer from a disease for a lifetime. In this proposed system, we developed an autonomous system that detects lumbar implants and diagnoses scoliosis from the modified Vietnamese x-ray imaging. We applied two different approaches including pre-trained APIs and transfer learning with their pre-trained models due to the unavailability of sufficient x-ray medical imaging. The results show that transfer learning is suitable for the modified Vietnamese x-ray imaging data as compared to the pre-trained API models. Moreover, we also explored and analyzed four transfer learning models and two pre-trained API models with our datasets in terms of accuracy, sensitivity, and specificity.