The demand for identifying a reliable person is increased because of security issues in our daily life. At present, to identify a person biometric technique such as face recognition is introduced. Since,a person with abnormal behaviour recognition system has reached certain level, their accomplishments in real time applications are restricted by challenges, such as illumination variations. The present visual recognition system is good at controlled illumination conditions and thermal face recognition system is better for detecting disguised persons or when there is no illumination control. Hence, a hybrid system which uses both visual and thermal images for recognising a person is better. The objective of this research is to implement a method which improves the quality of the image by fusing visual and thermal imaging images. Our research methodology has introduced to enhance servo line camera images. Nonlinear image transfer functions were introduced,and the parameters associated with those functions are determined by image statistics for making adaptive algorithms. Next methodswereintroduced for registering the visual images to their consequent thermal images. To get a transformation matrix for the registration, the landmarks in the images are first detected and a subset of those landmarks were selected to obtain the matrix, we propose a hybrid algorithm for detection, tracking and classification using OFSA algorithm to fuse the registered thermal and visual images. In this research, we focus on object detection using OFSA algorithm for more accuracy.

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PurposeComputed tomography (CT) scan can provide valuable information in the diagnosis of lung diseases. To detect the location of the cancerous lung nodules, this work uses novel deep learning methods. The majority of the early investigations used CT, magnetic resonance and mammography imaging. Using appropriate procedures, the professional doctor in this sector analyses these images to discover and diagnose the various degrees of lung cancer. All of the methods used to discover and detect cancer illnesses are time-consuming, expensive and stressful for the patients. To address all of these issues, appropriate deep learning approaches for analyzing these medical images, which included CT scan images, were utilized.Design/methodology/approachRadiologists currently employ chest CT scans to detect lung cancer at an early stage. In certain situations, radiologists' perception plays a critical role in identifying lung melanoma which is incorrectly detected. Deep learning is a new, capable and influential approach for predicting medical images. In this paper, the authors employed deep transfer learning algorithms for intelligent classification of lung nodules. Convolutional neural networks (VGG16, VGG19, MobileNet and DenseNet169) are used to constrain the input and output layers of a chest CT scan image dataset.FindingsThe collection includes normal chest CT scan pictures as well as images from two kinds of lung cancer, squamous and adenocarcinoma impacted chest CT scan images. According to the confusion matrix results, the VGG16 transfer learning technique has the highest accuracy in lung cancer classification with 91.28% accuracy, followed by VGG19 with 89.39%, MobileNet with 85.60% and DenseNet169 with 83.71% accuracy, which is analyzed using Google Collaborator.Originality/valueThe proposed approach using VGG16 maximizes the classification accuracy when compared to VGG19, MobileNet and DenseNet169. The results are validated by computing the confusion matrix for each network type.

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