Artificial intelligence (AI) based disease identification has the potential to transform medicine by utilizing machine learning algorithms and techniques to analyze large volumes of medical data and identify patterns and features that may be difficult for human experts to detect. However, there are still challenges and limitations to overcome, such as the need for high-quality medical data and concerns around privacy and bias. This chapter explores the growing intersection of machine learning (ML) and AI techniques with disease prediction. The chapter begins by providing an overview of ML and AI methodologies commonly employed in disease prediction, including supervised and unsupervised learning algorithms, deep learning techniques, and ensemble methods. Lastly, the chapter outlines future directions and research opportunities in the field.

Background: We propose a novel global region based method for segmentation of biomedical images with a fast level set model using Kernel Fuzzy Particles Swarm Optimization (KFPSO) clustering. In the field of medical study, segmentation of biomedical images is one of the most important approaches for the diagnosis of a patient. Segmentation of these images is still a tedious task and cumbersome due to weak contrast and poor resolution of images etc. The automatic segmentation of such images is very difficult. The main reason is a large amount of inhomogeneity present in the background and foreground of real world image. The conventional methods like C-V model and Distance Regularized Level Set (DRLS) method lead to getting improper segmentation with unconvinced results. Methods: We proposed an efficient segmentation method for MRI biomedical images with level set approach using KFPSO clustering. In the pre-processing step, Kernel Fuzzy C- Means (KFCM) clustering is combined with PSO Initialization algorithm called KFPSO clustering for improving the clustering efficiency. In KFCM clustering algorithm, the initial cluster center is chosen randomly, and with the help of PSO, the optimal cluster centers are chosen. Results: The Membership Function (MF) of this algorithm is less sensitive to noise and consideration of the spatial information. In the post processing step, the resulting Kernel fuzzy PSO clustering is modified to the level set model for faster and accurate segmentation. Conclusion: The Proposed segmentation results are effective, superior and accurate compared to conventional methods. This new approach is very helpful for accurate detection of the white matter and gray matter, cancerous cells in brain and bone images.

In recent years, image segmentation based on level set formulations was encompassed by proficiently implementing various image processing applications, especially in case of reformative models for medical image segmentation. Existing level set methods under this criterion are not up to the mark in terms of noise, weak boundaries, and inhomogeneity. In this paper, we put forward a new formulation for selective segmentation with level set model using optimised fuzzy region clustering and implementation is carried out in two stages. In first, optimised fuzzy region clustering (OFR) is employed in which particle swarm optimisation technique is utilised at the initialisation of FCM clustering to obtain optimal cluster centres and then it is incorporated with region competition. While in second, implementing OFR clustering using level set methods. The proposed formulation is successfully implemented on some contemporary medicinal images. It is capable to identify the weak boundaries and segment the desired components of an image such as white substance, grey substance, cerebrospinal fluid, bone sarcoma, breast cancer and Alzheimer’s disease, etc. The visual results and quantitative analysis show its effectiveness, superiority and accuracy over the existing formulations.

Images that are obtained in the real world in low contrast are inappropriate for human eyes to read the medical images. Enhancement and segmentation have an important role to play in digital image processing, pattern recognition, and the computer vision. Here, this paper presents an effective way of changing histograms and improving contrast in digital images. Segmentation is done on AGCWD enhanced images. Histogram equalization is an important technique for contrast enhancement. Nevertheless, modern Histogram Equalization commonly results in unnecessary contrast enhancement, which in turn offers an un-natural presence to the processed image and produces visual artifacts. We present an automated transformation technique that helps boost dimmed image brightness by gamma correction and weighted distribution, commonly known as Adaptive Gamma Correction Weighted Distribution (AGCWD). The contrast enhancement level can be modified using this technique; noise robustness, white or black stretching, and the protection of medium brightness can be easily integrated into the optimization process. Finally, a contrast enhancement algorithm with low complexity is introduced. All the process of enhancement will be done during the process of pre-processing the image. Later, in post-processing, we introduce a specific level set method known as ORACM for better segmentation of an enhanced AGCWD image, and it is compared with the traditional level set method.

This paper proposes a hybrid algorithm for segmenting the layers and dark sand dune regions in Mars satellite images, using particle swarm optimization (PSO)-based adaptive K-means clustering with the level set model. These varieties of images are taken from the surface of Mars orbit. Because of the presence of layers and dark sand dune regions in these images, automatic segmentation of these images is very complex when identifying these regions. Segmentation of such images may lead to improper results with conventional level set. We propose a level set evolution based on the PSO with an adaptive K-means clustering algorithm for segmentation of these images. In adaptive K-means, there is no requirement to mention the number when clustering in advance as it generates consistent output. Using binary PSO, the “best” number of clusters is selected. The centres of the chosen clusters is refined via the adaptive K-means clustering to find a new “optimal” number of clusters automatically in the corresponding images to get a more precise clustering efficiency. This is achieved by PSO-based adaptive K-means clustered images which are hybridized with a new signed pressure function. This approach is successfully demonstrated on Mars satellite images, this experiments have shown superiority over previous techniques in terms of more robust, accurate and faster.

In this paper, we proposed a novel global region based segmentation method for satellite and medical images with geometric active contour model and level set evolution on noisy images with salt and pepper. The active contour or snake model is one of the most successful variational models in image segmentation. It has been widely used to locate boundaries of image segmentation and computer vision. Problem associated with the existence of the local minima in the active contour energy function makes snakes have poor convergence in segmentation process; therefore, the poor convergence has limited applications. In this work, a fast minimization of snake model is used for satellite and medical image segmentation on noisy images with ten percentage of Noisy was added. This method provides a satisfied result. As a result, it is a good candidate for medical image segmentation approach. Experiments on satellite images with noise demonstrate the advantages of the proposed method over the Chan-Vase (CV) active contour in terms of the number of Iterations and time complexity are less because it uses isotropic schemes to regularize the contour and is sub-pixel precise. Finally, the Memory requirement is low.