A systematic review on feature extraction methods and deep learning models for detection of cancerous lung nodules at an early stage -the recent trends and challenges.

Lung cancer is one of the most common life-threatening worldwide cancers affecting both the male and the female populations. The appearance of nodules in the scan image is an early indication of the development of cancer cells in the lung. The Low Dose Computed Tomography screening technique is used for the early detection of cancer nodules.

Analysis of banana plant health using machine learning techniques.

The Indian economy is greatly influenced by the Banana Industry, necessitating advancements in agricultural farming. Recent research emphasizes the imperative nature of addressing diseases that impact Banana Plants, with a particular focus on early detection to safeguard production. The urgency of early identification is underscored by the fact that diseases predominantly affect banana plant leaves.

An extensive analysis of artificial intelligence and segmentation methods transforming cancer recognition in medical imaging.

Recent advancements in computational intelligence, deep learning, and computer-aided detection have had a significant impact on the field of medical imaging. The task of image segmentation, which involves accurately interpreting and identifying the content of an image, has garnered much attention. The main objective of this task is to separate objects from the background, thereby simplifying and enhancing the significance of the image.

A comparative analysis of CNN-based deep learning architectures for early diagnosis of bone cancer using CT images.

Bone cancer is a rare in which cells in the bone grow out of control, resulting in destroying the normal bone tissue. A benign type of bone cancer is harmless and does not spread to other body parts, whereas a malignant type can spread to other body parts and might be harmful. According to Cancer Research UK (2021), the survival rate for patients with bone cancer is 40% and early detection can increase the chances of survival by providing treatment at the initial stages.

Classifying Unstable and Stable Walking Patterns Using Electroencephalography Signals and Machine Learning Algorithms.

Analyzing unstable gait patterns from Electroencephalography (EEG) signals is vital to develop real-time brain-computer interface (BCI) systems to prevent falls and associated injuries. This study investigates the feasibility of classification algorithms to detect walking instability utilizing EEG signals. A 64-channel Brain Vision EEG system was used to acquire EEG signals from 13 healthy adults.

Analysis of Security Issues in Wireless Body Area Networks in Heterogeneous Networks.

Body Area Network (BAN) is one of the most important techniques for observing patient health in real time and identifying and analyzing diseases. For effective implementation of this technology in practice and to benefit from it, there are some key issues which are to be addressed, and among those issues, security is highly critical. WBAN will have to operate in a cooperative networking model of multiple networks such as those of homogeneous networks, for the purpose of performance and reliability, or those of heterogeneous networks, for the purpose of data transfer and processing from application point of view, with the other networks such as the networks of hospitals, clinics, medical experts, etc.