Background: Lung cancer is the second most common cancer worldwide, with over two million new cases per year. Early identification would allow healthcare practitioners to handle it more effectively. The advancement of computer-aided detection systems significantly impacted clinical analysis and decision-making on human disease. Towards this, machine learning and deep learning techniques are successfully being applied. Due to several advantages, transfer learning has become popular for disease detection based on image data. Methods: In this work, we build a novel transfer learning model (VER-Net) by stacking three different transfer learning models to detect lung cancer using lung CT scan images. The model is trained to map the CT scan images with four lung cancer classes. Various measures, such as image preprocessing, data augmentation, and hyperparameter tuning, are taken to improve the efficacy of VER-Net. All the models are trained and evaluated using multiclass classifications chest CT images. Results: The experimental results confirm that VER-Net outperformed the other eight transfer learning models compared with. VER-Net scored 91%, 92%, 91%, and 91.3% when tested for accuracy, precision, recall, and F1-score, respectively. Compared to the state-of-the-art, VER-Net has better accuracy. Conclusion: VER-Net is not only effectively used for lung cancer detection but may also be useful for other diseases for which CT scan images are available.

Lung cancer is a dangerous disease in human health. At the early stage, lung cancer detection provides a way to save human life. As a result, improvements in Deep Learning (DL), a technique, a branch of Machine Learning (ML), have helped to identify and classify lung cancer in clinical photographs. DL technology has also outperformed traditional methods in a variety of fields. Researchers are exploring various DL techniques for disease detection to improve the accuracy of the CAD systems in CT lung cancer detection. In this experiment, cutting-edge ML and DL methods for lung disease have been recommended as CAD systems after thoroughly analysing existing frameworks. It can be separated into FP reduction systems and system to detect nodule. The primary characteristics of various approaches are analyzed. The CT lung datasets existing for examination and evaluation with the various approaches are also presented and discussed.

Agriculture is the main factor for economy and contributes to GDP. The growth of the economy of many countries is based on agriculture. As a result, the yield factor, quality and volume of agricultural products, play a critical role in economic development. Plant diseases and pests have become a major determinant of crop yields throughout the years, as such illnesses in plants offer a serious threat and impediment to higher yields or production in the agriculture industry. As a result, From the outset, it becomes the major duty to correctly monitor the plants, to detect diseases thoroughly, and to determine methods of controlling or monitoring these plant diseases pests in order to achieve a higher rate of production growth and minimal crop damage. Using machine vision, deep learning methods and tools for extracting and classifying features, It could be possible to build a reliable disease detection system. Numerous researchers have created and deployed various ways for detecting plant diseases and pests. The potential of these methods has been examined in this work.

Varieties of serious mental and physical disorders are the cause of variations in gait. Gait analysis is extensively used in a variety of clinical applications to diagnose and monitor specific disorders. Sports, physical rehabilitation, clinical evaluation, surveillance, identification, modeling, and other industries all benefit from gait analysis. The study provides extensive information on characteristics, types, methodologies, limitations, applications, datasets, and tools used in gait analysis employing different sensor-based and vision-based approaches. A thorough study on gait analysis indicates a significant research gap in various elements of vision-based gait analysis. The field is either undiscovered or has received minimal attention in various scenarios, thus requiring emphasis on comprehensive analysis and exploration. This study will help analyze human walking patterns concerning clinical applications, rehabilitation, injury assessment, and fall risk assessment. It can provide important insights into various aspects of a person’s gait.