The era of intelligent algorithms has arrived, and machine learning is one of the most promising technologies to revolutionize healthcare. Until recently, manufacturing, transportation, and administration were the primary industries where machine learning algorithms had a significant impact. However, even formerly impervious industries like healthcare are suddenly being affected by these algorithms. While machine learning has been around for quite some time, its use in healthcare is continuously increasing alongside the availability of data. It is a statistical method that allows computers to learn from past data. They are able to identify patterns and come to conclusions or judgments depending on the information that they are presented with. Machine learning (ML) has numerous prospective applications within the healthcare industry. They extend from drug discovery to clinical decision-making and diagnosis. There are petabytes of healthcare-related data that require analysis. For instance, the human genome is an example of this, which is approximately 100 gigabytes per person. Furthermore, carry-and-wear devices generate a large quantity of data, including heart rate, blood pressure, and walking pattern. Therefore, on the basis of these data, ML techniques can be used to predict diseases and develop personalized treatments. Moreover, X-ray and MRI image classification techniques can be used to construct an ML algorithm for potential disease diagnosing, thereby reducing the burden on clinicians. Likewise, in drug discovery and development, ML algorithms have been utilized to help identify novel therapeutic targets, design new drug candidates, and predict drug toxicity. ML techniques can be used to create predictive models for patient outcomes like mortality, readmission, and disease progression. ML algorithms can be put to use to analyze electronic health record (EHR) data to facilitate clinical decision-making, such as predicting patient readmission rates or identifying patients who may benefit from a specific treatment. Therefore, ML has the potential to revolutionize the healthcare industry by providing methods to cluster, classify, predict, and assist clinicians in making informed decisions. Consequently, this chapter will investigate the current state of machine learning (ML) in the healthcare industry, as well as the challenges it faces and its future development potential.

Machine learning (ML), a subset of artificial intelligence (AI), isrevolutionizing industries by leveraging statistical algorithms that learn from data andexperiences. Unlike traditional programs following predetermined sequences, MLalgorithms discern patterns and predict outcomes through extensive datasets. Thistransformative technology has profoundly impacted diverse sectors, includingmanufacturing, finance, retail, transportation, entertainment, and healthcare. Theinfluence of ML is amplified by the accessibility of extensive datasets and theescalating computational prowess of modern systems. As ML algorithms progress, theyare fundamentally reshaping business operations, streamlining processes, enhancingdecision-making, and fuelling innovation across sectors. The impact of machinelearning algorithms on healthcare applications and the usage of diverse data sources,such as electronic health records, medical imaging, wearable devices, and genomicdata, is discussed in this chapter.

Deep learning (DL) is a specialized area within the field of machine learning (ML) that focuses on training models using deep neural networks. Specifically, deep learning with convolutional neural networks (CNN) incorporates convolutional layers on top of neural networks to effectively extract spatial features from images, making them suitable for tasks such as image classification and object detection. The availability of abundant computational power and vast amounts of data has led to the successful training of deep CNN models for accurate image classification. Consequently, the utilization of deep CNN in the healthcare sector has significantly influenced various aspects, including disease diagnosis, aiding physicians in clinical decision-making, continuous patient monitoring, and the development of personalized treatment approaches. In this chapter, we will explore several use cases of deep CNN networks in the healthcare industry, assessing their impact and considering the associated ethical considerations.

In the digital era, low-cost hardware like sensors and cameras has led to the creation of numerous image databases for various applications. This has led to the need for retrieval systems that rely on visual content, and these types of systems are called content-based image retrieval (CBIR) systems. It’s a method utilized to locate and extract digital images from extensive databases by considering their visual attributes, as opposed to relying exclusively on metadata or written descriptions. In order to obtain appropriate images from the database, features including colour histograms, texture patterns, and shape descriptors are being used to determine similarities between the images. Over the course of the last twenty years, efforts have been directed towards creating hand-crafted features tailored for CBIR systems. However, depending solely on distance-based retrieval methods is a formidable task. Hence, this study strives to leverage the capabilities of classifiers as well for the purpose of retrieval. So, the proposed CBIR paradigm uses not only the hand-crafted features but also the strength of the classifier with weighted distance metricTherefore, the proposed CBIR paradigm is designed in a way that it uses the strength of the NaiveBayes classifier to compute weighted distance using hand-crafted wavelet features to get similar images from the database. The performance of the proposed method is evaluated on three most popular texture datasets and found to be better among all the methods reported in this work

With rapid growth of social media platforms and widespread use of handheld devices such as mobile phones and video cameras, the number of videos being captured and shared over the internet has increased significantly. However, due to the lack of organization, most of these videos lack semantic context. Traditional methods of video retrieval involve searching for relevant videos using attached semantics. which has led to the need for content-based video retrieval, where video contents are utilized for searching, whether by video or text queries.The primary goal of our system is to provide relevant videos from a database. Our proposed approach in this paper employs Pearson’s coefficient of correlation (PCC) for key frame extraction from videos, subsequently building a feature vector that represents the video’s content. We have also experimented with linear binary pattern (LBP) and Colour moments (CM). We have used precision metric for evaluating performance. For conducting experiments, we utilized the UCF101 dataset, comprising 13,320 videos across 101 categories

Content-Based Remote Sensing Image Retrieval (CBRSIR) is used to find relevant images from large collections of remote sensing images. CBRSIR works by indexing each image in the database with a feature vector. Deep semantic features generated using convolutional neural networks (CNNs) are more powerful than low-level features for CBRSIR tasks because they can comprehend the context and content within an image. However, the major problem with the deep features is its large vector size which in turn can impact the performance of the retrieval system and are more susceptible to noise and outlier data. Therefore, in this work, a modified ResNet50 architecture is proposed that serves as a powerful feature extractor, benefiting from its deep learning capabilities. Specific modifications are introduced to enhance its discriminative power and generalization ability, enabling it to extract more robust deep features for image indexing. The proposed method achieves a mean average precision (mAP) of 0.899 surpassing the popular competing methods ResNet50 and GoogleNet by a substantial margin of 22.02%, 26.79% respectively. Moreover, to address the curse of dimensionality, this study also proposes a novel approach that combines a modified ResNet50 architecture with Linear Discriminant Analysis (LDA) and Maximum Relevance and Minimum Redundancy (MRMR) technique. The proposed approach achieves 85.45% reduction in size of the feature vector using MRMR and 98.19% using LDA, thereby improving retrieval efficiency without impacting the performance.

Image classification stands as a fundamental task in computer vision, and Convolutional Neural Networks (CNNs) have emerged as highly proficient tools, demonstrating remarkable accuracy and performance. However, with the increasing complexity and diversity of image datasets, there is a growing need to improve the robustness and generalization of CNN-based classifiers. One promising approach to address this challenge is the ensembling of CNNs. Ensembling involves combining the outputs of multiple CNNs to enhance classification performance. This technique leverages the strength and diversity of individual models to achieve superior results compared to using a single model alone. Therefore, GLS-NET, an ensemble framework is proposed which uses three parallel ResNet50 CNNs and takes different features as input so as to induce the diversity in data which in turn can learn discriminative features to produce high accuracy. The proposed framework is evaluated on the most popular dataset, EMNIST, and achieved good performance improvement in accuracy. EMNIST is the most popular dataset used extensively in evaluating the performance of many deep learning techniques.

In the modern digital era, with the availability of low-cost hardware like sensors and cameras, a huge amount of image databases are being created for diverse applications. These databases give rise to the need of developing efficient content-based image retrieval (CBIR) systems. Major efforts have been put over the past two decades to develop different global and low-level texture features to build efficient CBIR systems. However, designing texture features that are suitable for distance-based retrieval is always a challenging task. Recently, Convolution Neural Networks have shown promising results for object detection and classification. CNNs are also applied to build classifier-based retrieval systems. However, the classifier-based retrieval methods can retrieve images only from the predicted class. Therefore, the performance of such system greatly depends on classification performance of the classifier. This paper proposes a method that exploits the strength of the Convolutional Neural Networks for predicting the class membership of the query image for all output classes and retrieve images using a modified distance function in the wavelet feature space. The performance of the proposed method is evaluated using three popular texture datasets of varying complexity and found to be superior to all competing methods considered.