Machine learning is quickly becoming an important tool for diagnosis and prognosis of various medical conditions. Complex input output mappings are dealt in deep learning, which is developed based on machine learning approach. Due to its efficiency and similarity to the working of the human brain, deep neural networks are a preferred method of processing and analysing medical data. In addition to diagnosis, deep learning is used to study the progression of disease, develop a personalised treatment plan and for overall patient management. This chapter discusses the architecture and working of deep neural networks and focus on its application in the detection and treatment of various diseases like cancer, diabetes, Alzheimer’s and Parkinson’s disease.

Deep Learning in Healthcare

Nowadays, data is generated all the time on the internet. Faced with this scenario, technologies have emerged to take advantage of this feature, so that in addition to just being able to measure and understand where they come from, it is possible for them to be collected, quantified, decoded, and analyzed, allowing the understanding of behaviors and trends, the definition of strategies, and the process of insight generation. Big data leverages resources that organize and catalog this information, increasing the availability of relevant data for informed decision making. Machine learning is an aspect of artificial intelligence that competently performs automation in the process of building analytical models that allow machines to adapt independently to new scenarios, enabling software to successfully predict and react to the deployment of scenarios based on past results. Deep learning has this nomenclature because it deals with neural networks having multiple (deep) layers that allow learning; therefore it is a subset of machine learning, which considers algorithms inspired by the human brain, the artificial neural networks, which learn from large amounts of data. Deep learning techniques are especially useful for analyzing complex, rich, and multidimensional data such as voice, images, and videos. In short, all deep learning is machine learning, but not all machine learning is deep learning. This chapter examines the technology of deep learning and machine learning in big data by addressing its evolution and fundamental concepts and its integration into new technologies, by approaching its success, and by categorizing and synthesizing the potential of both technologies.

An overview of deep learning in big data, image, and signal processing in the modern digital age

Improving diagnostics has been one of the most important challenges facing health systems in recent years, due to innovation and scientific research, image classification techniques, medical information management systems brought about by health technology, and developments never seen before that enable major breakthroughs in the areas of prevention, early disease detection, and disease control Blood is investigated through blood count, which is subordinate to manual and/or automated procedures This reliance on medical fields for new innovations directs this chapter to the development of a deep learning framework for classifying white cell subtypes in digital images that fits the criteria for reliability and efficiency of blood cell detection, making the methodology more accessible to diverse populations Because laboratory medical examinations are often costly and inaccessible to populations of underdeveloped and developing countries, it is of great importance to create tools that facilitate the obtaining of medical reports at low cost and with high reliability For this, Python using a Jupyter notebook was developed and experiments were conducted using a dataset containing 12,500 digital images of human blood smear fields comprising nonpathological leukocytes As a result, the accuracy of the approach was 8617%, demonstrating the high reliability of the methodology Therefore the algorithm is considered a reliable, accurate, and inexpensive method and can be employed as the third most practicable procedure for blood counts for often underprivileged people of developing and underdeveloped countries

Deep learning methodology proposal for the classification of erythrocytes and leukocytes

Classification of Pest in Tomato Plants Using CNN

Throughout this chapter the objective is to bring deep learning techniques and algorithms, specifically CNN, which bring about ease for a researcher with respect to time and resources. The concepts are explained as an overview to implant an intuition of the techniques which can be further elaborated with the mathematics in the references. Computational biology involves the examination of how proteins interact with each other through the simulation of protein folding, motion, and interaction. Current computational biology research can be divided into a number of broad areas, mainly based on the type of experimental data that is analyzed or modeled. Deep learning and in particular, Convolutional Neural Networks (CNNs) has brought about a revolution for the analysis of gene expression images. This technique solves some of the setbacks faced by traditional machine learning approaches while advances in technology have enabled the capture of gene sequence images, while in some cases non-image data captured can be converted to an image for analysis. 

Computational Biology in the Lens of CNN

The explosive growth of data, images in the World Wide Web makes it critical to the information retrievals. Image retrieval has been recognized as an elementary problem in the retrieval tasks and this exercise has got a wide attention based on the underlying domain characteristics. For instance, in social media data encompasses of noisy, diverse, heterogeneous, interconnected data. To confront these numerous characteristics and employ image retrieval the widely accepted deep architecture concept is utilized with the help of natural language latent query features. In this paper, we are introducing a novel approach for image retrieval task which collaboratively make use of the technicalities of natural language processing and deep architecture.

Image retrieval using latent feature learning by deep architecture

Information retrieval can be visualized as the extraction of the desired information from the flooded resources that spread over world wide web. Image retrieval is the fundamental and critical problem that arises in the retrieval activities. In this regard, it is considered to be a challenging task which requires utmost care. Diverse characteristics of data such as noisy, heterogeneity impose a great barrier over image retrieval applications. This chapter aims to come up with a state-of-the-art approach for overcoming these problems by clubbing together widely recognized deep architecture along with natural language processing. This novel design methodology utilizes the latent query features, deep belief network, restricted Boltzmann machine for learning tasks. This collaborative work can be used to reduce the epoch in the learning periods whereas the rest of the methods fail to achieve the constraints.

Nikhitha P

Papers

Image retrieval using latent feature learning by deep architecture

An Insight Into Deep Learning Architectures