Medical Diagnosis Using Hybrid of Machine Learning and Deep Learning Techniques

The medical diagnosis of diseases involves the detection and classification of patients from medical image data. It has an important Artificial Intelligence (AI) application in Healthcare and Neuroscience fields. In addition, Machine Learning (ML) and Deep Learning (DL) techniques provide important tasks on Neurodegenerative Diseases (ND) diagnosis from Brain MRI (Magnetic Resonance Imaging) data for image processing with good classification results. This chapter aims at comprehensive and comparative study on the performance of medical image classification with those techniques using Brain MRI datasets. We introduce a brief overview of the medical diagnosis of ND with AI applications in the neuroscience domain. In the literature part, we present the understanding of the medical image classification with Machine and Deep Learning techniques. Therefore, we describe the concept of MRI data and the Brain MRI datasets that are used in this work. In the comparison part, we focus on related works of the Artificial Neural Network (ANN) and the Deep Convolutional Neural Network (DCNN) models and we compare these models in the literature. In the methodology part, we combine the ANN and DCNN in order to propose a new Deep model named ANN-DCNN model when it is more accurate and effective to classify from those Brain images. To improve the performance of those Neural Networks in medical diagnosis applications, we discuss the recent insights and future directions.

Millions of people worldwide suffer from Parkinson’s disease (PD), a neurodegenerative disorder marked by motor symptoms such as tremors, bradykinesia, and stiffness. Accurate early diagnosis is crucial for effective management and treatment. This article presents a novel review of Machine Learning (ML) and Deep Learning (DL) techniques for PD detection and progression monitoring, offering new perspectives by integrating diverse data sources. We examine the public datasets recently used in studies, including audio recordings, gait analysis, and medical imaging. We discuss the preprocessing methods applied, the state-of-the-art models utilized, and their performance. Our evaluation included different algorithms such as support vector machines (SVM), random forests (RF), convolutional neural networks (CNN). These algorithms have shown promising results in PD diagnosis with accuracy rates exceeding 99% in some studies combining data sources. Our analysis particularly showcases the effectiveness of audio analysis in early symptom detection and gait analysis, including the Unified Parkinson’s Disease Rating Scale (UPDRS), in monitoring disease progression. Medical imaging, enhanced by DL techniques, has improved the identification of PD. The application of ML and DL in PD research offers significant potential for improving diagnostic accuracy. However, challenges like the need for large and diverse datasets, data privacy concerns, and data quality in healthcare remain. Additionally, developing explainable AI is crucial to ensure that clinicians can trust and understand ML and DL models. Our review highlights these key challenges that must be addressed to enhance the robustness and applicability of AI models in PD diagnosis, setting the groundwork for future research to overcome these obstacles.

Breast cancer is one of the most prevalent and chronic disease that affect women. To overcome this disease, effective medical treatment is required. Early detection of the disease plays an important role for suitable medication and survival of patient. To identify the breast cancer in the patients, standard imaging technique mammography is used. Due to the subtle and varied nature of cancer tissues interpreting mammogram images can be a challenge to doctors. Machine learning (ML) and Deep Learning (DL) techniques offer promising solutions that provide efficient breast cancer detection from mammograms. In this review paper a comprehensive review of ML and DL algorithms and their applications in mammogram image analysis are presented. Various supervised and unsupervised learning techniques, such as convolutional neural networks (CNNs), support vector machines (SVMs), random forests, and other popular ML and DL models are discussed in paper. The integration of these DL methods that are efficiently used in image preprocessing techniques, feature extraction, and classification strategies. The overall survey focusses on various performance metrics, datasets, and benchmarks used in existing studies. Further the strengths and limitations of different approaches used by various researchers are identified. By understating current research trends this paper aims to contribute to the ongoing development of more accurate and reliable breast cancer detection systems using advanced ML techniques.

A comprehensive review on detection of plant disease using machine learning and deep learning approaches

Deep and machine learning techniques for medical imaging-based breast cancer: A comprehensive review

Agriculture has been a vital sector for the majority of people, especially in countries like India. However, the increasing need for food production has led to intensive farming practices that have resulted in the deterioration of soil quality. This deterioration in soil quality poses significant challenges to both agricultural productivity and environmental sustainability. To address these challenges, advanced soil nutrient prediction systems that utilize machine learning and deep learning techniques are being developed. These advanced soil nutrient prediction systems utilize various sources of data, such as soil parameters, plant diseases, pests, fertilizer usage, and changes in weather patterns. By mapping and analyzing these data sources, machine learning algorithms can accurately predict the distribution of soil nutrients and other properties essential for precise agricultural practices. A previous study compared machine learning algorithms like SVM and Random Forest with deep learning algorithms CNN and LSTM for predicting crop yields. The most appropriate model is a significant challenge, but several studies have evaluated recommendation system models using deep machine learning techniques. Deep learning models attain accuracy above 90%, while many ML models achieve rates between 90% and 93%. Furthermore, the research seeks to propose specific crop suggestions grounded in soil nutrients for precision agriculture to enhance crop productivity.

Coastal aquifers are vital for sustaining ecosystems and providing freshwater for agriculture and domestic use. However, rising groundwater salinity in these regions demands innovative approaches for accurate modeling and prediction. In recent decades, machine learning (ML) and deep learning (DL) techniques have been extensively used across various water resource management fields, often yielding promising outcomes. This review examines the growing usage of ML and DL techniques in modeling groundwater salinity in coastal aquifers. The analysis is based on 104 peer-reviewed journal articles including the review articles indexed in PubMed, ScienceDirect, Google Scholar, Scopus, and SpringerLink using keywords like "saltwater intrusion", "machine learning", “deep learning”, and "groundwater salinity". The review discusses recent advancements, challenges, and future directions for improving ML model accuracy and applicability. Studies reveal an increasing reliance on ML techniques to predict groundwater salinity and manage seawater intrusion across various coastal geological settings. Researchers have employed a range of ML models, from traditional methods like Artificial Neural Networks and Adaptive Neuro Fuzzy Inference Systems to advanced DL-based models like Convolutional Neural Networks and Long Short-Term Memory Networks, often using ensemble methods to enhance accuracy. Key findings include the top-tier output of ensemble and optimized models, including Random Forest and Grasshopper Optimization Algorithm-XGBoost. These studies highlight the importance of model selection, input variable ranking, and computational efficiency, demonstrating ML's global relevance in environmental management. The review highlights the prospects of ML, DL, and ensemble models to redefine groundwater management in coastal aquifers, offering more effective and sustainable solutions to the challenges posed by groundwater salinity and saltwater intrusion. Future advancements will focus on further integrating DL, explainable artificial intelligence, and ensemble approaches, along with other innovative techniques, to explore sparsely studied variables, model new or unique study areas, and apply ML methods for managing groundwater salinity.

The Internet of Things (IoT) has transformed many aspects of modern life, from healthcare and transportation to home automation and industrial control systems. However, the increasing number of connected devices has also led to an increase in security threats, particularly from botnets. To mitigate these threats, various machine learning (ML) and deep learning (DL) techniques have been proposed for IoT botnet attack detection. This systematic review aims to identify the most effective ML and DL techniques for detecting IoT botnets by delving into benchmark datasets, evaluation metrics, and data pre-processing techniques in detail. A comprehensive search was conducted in multiple databases for primary studies published between 2018 and 2023. Studies were included if they reported the use of ML or DL techniques for IoT botnet detection. After screening 1,567 records, 25 studies were included in the final review. The findings suggest that ML and DL techniques show promising results in detecting IoT botnet attacks, outperforming traditional signature-based methods. However, the effectiveness of the techniques varied depending on the dataset, features, and evaluation metrics used. Based on the synthesis of the findings, this review proposes a taxonomy for ML and DL techniques in IoT botnet attack detection and provides recommendations for future research in this area. This review illuminates the considerable potential of ML and DL approaches in bolstering the detection of IoT botnet attacks, thereby offering valuable insights to researchers involved in the domain of IoT security.

Kidney stone detection is a critical healthcare challenge, as timely and accurate diagnosis can prevent complications. The motivation behind this review is the increasing prevalence of kidney stones and the need for more effective, non-invasive detection methods. Machine learning (ML) and deep learning (DL) techniques offer promising solutions, leveraging medical imaging data to enhance diagnostic accuracy. However, limitations such as high computational cost and reliance on large datasets hinder their full potential. The aim of this review is to analyze the latest advancements in kidney stone detection using ML and DL techniques. The objective is to compare existing methodologies, highlight their strengths and weaknesses, and suggest future research directions, particularly in integrating transfer learning and fine-tuning techniques to enhance performance.

The detection of lung and colon cancer is a critical challenge in medical diagnosis, and machine learning (ML) and deep learning (DL) techniques are increasingly being used to enhance accuracy and efficiency. This review focuses on the integration of ML and DL methods for the combined detection of lung and colon cancer, emphasizing their strengths, limitations, and future potential. The motivation behind this study is to address the growing demand for accurate and early detection of these cancers, which significantly impacts treatment outcomes. Current methods often struggle with feature complexity, image variability, and computational intensity, which limit their real-world applicability. The aim is to consolidate various ML and DL techniques that have been employed for this purpose, highlighting how hybrid models can improve detection rates. The objective of this review is to provide a comprehensive analysis of different methodologies, their datasets, pre-processing techniques, feature extraction methods, and evaluation parameters. This review also explores recent advancements, such as transfer learning combined with fine-tuning techniques, which can further optimize performance in cancer detection. The findings suggest that while current methods show promise, further improvements in model generalization, interpretability, and computational efficiency are required to overcome existing limitations and expand clinical use.

A novel approach for water quality classification based on the integration of deep learning and feature extraction techniques

Population growth and urbanization have led to a significant increase in solid waste. However, conventional methods of treating and recycling this waste have inherent problems, such as low efficiency, poor precision, high cost, and severe environmental hazards. To address these challenges, Artificial Intelligence (AI) has gained popularity in recent years as a potential solution for municipal solid-waste management (MSWM). A few applications of AI, based on Machine Learning (ML) and Deep Learning (DL) techniques, have been used for MSWM. This study reviews the current landscape in MSWM, highlighting the existing advantages and disadvantages of 69 studies published between 2018 and 2024 using the PRISMA methodology. The applications of ML and DL algorithms demonstrate their ability to enhance decision-making processes, improve resource recovery rates, and promote circular economy principles. Although these technologies offer promising solutions, challenges such as data availability, quality, and interdisciplinary collaboration hinder their effective implementation. The paper suggests future research directions focusing on developing robust datasets, fostering partnerships across sectors, and integrating advanced technologies with traditional waste management strategies. This research aligns with the United Nations’ Sustainable Development Goals (SDG), particularly Goal 11, which aims to make cities inclusive, safe, resilient, and sustainable. In the future, this research can contribute to making cities smarter, greener, and more resilient using ML and DL techniques.

Smart farming leverages Artificial Intelligence (AI) to address modern agricultural sustainability challenges. This study investigates the application of machine learning (ML), deep learning (DL), and time series analysis in agriculture through a systematic literature review following the PRISMA methodology. The review highlights the critical roles of ML and DL techniques in optimizing agricultural processes, such as crop selection, yield prediction, soil compatibility classification, and water management. ML algorithms facilitate tasks like crop selection and soil fertility classification, while DL techniques contribute to forecasting crop production and commodity prices. Additionally, time series analysis is employed for demand forecasting of crops, commodity price prediction, and forecasting crop yield production. The focus of this article is to provide a comprehensive overview of ML and DL techniques within the farming industry. Utilizing crop datasets, ML algorithms are instrumental in classifying soil fertility, crop selection, and various other aspects. DL algorithms, when applied to farming data, enable effective time series analysis and crop selection. By synthesizing the integration of these technologies, this review underscores their potential to enhance decision-making in agriculture and mitigate food scarcity challenges in the future.

This review offers an in-depth examination of Deep Learning (DL) and Machine Learning (ML) techniques for smart grid load forecasting, emphasizing language precision, methodological rigor, and the exploration of novel contributions. The language used in this review is both technical and accessible, balancing complex concepts with clear explanations to cater to both specialists and general readers. It meticulously dissects contemporary DL models, including neural networks and ensemble methods, and evaluates their effectiveness through a detailed review of algorithms and frameworks. The methodology section systematically compares these techniques against traditional forecasting methods using performance metrics such as MAPE, RMSE, and MSE, ensuring a comprehensive assessment of their accuracy and scalability. A significant contribution of this review is its examination of real-world applications and case studies, which demonstrate how ML and DL techniques address practical challenges in energy management, such as grid stability and demand forecasting. Furthermore, the review introduces novel perspectives on the integration of probabilistic forecasting and ensemble methods, which offer innovative approaches for managing energy demand uncertainties. By identifying current limitations and proposing future research directions, this review not only advances the understanding of DL and ML applications in smart grids but also provides a foundation for future developments in this evolving field.

Feature selection (FS) plays a crucial role in medical imaging by reducing dimensionality, improving computational efficiency, and enhancing diagnostic accuracy. Traditional FS techniques, including filter, wrapper, and embedded methods, have been widely used but often struggle with high-dimensional and heterogeneous medical imaging data. Deep learning-based FS methods, particularly Convolutional Neural Networks (CNNs) and autoencoders, have demonstrated superior performance but lack interpretability. Hybrid approaches that combine classical and deep learning techniques have emerged as a promising solution, offering improved accuracy and explainability. Furthermore, integrating multi-modal imaging data (e.g., Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Positron Emission Tomography (PET), and Ultrasound (US)) poses additional challenges in FS, necessitating advanced feature fusion strategies. Multi-modal feature fusion combines information from different imaging modalities to improve diagnostic accuracy. Recently, quantum computing has gained attention as a revolutionary approach for FS, providing the potential to handle high-dimensional medical data more efficiently. This systematic literature review comprehensively examines classical, Deep Learning (DL), hybrid, and quantum-based FS techniques in medical imaging. Key outcomes include a structured taxonomy of FS methods, a critical evaluation of their performance across modalities, and identification of core challenges such as computational burden, interpretability, and ethical considerations. Future research directions—such as explainable AI (XAI), federated learning, and quantum-enhanced FS—are also emphasized to bridge the current gaps. This review provides actionable insights for developing scalable, interpretable, and clinically applicable FS methods in the evolving landscape of medical imaging.