Multi-Class Pneumonia Classification Using Transfer Deep Learning Methods

Medical image classification, a critical component in medical diagnostics, has significantly advanced through the integration of machine learning (ML) and deep learning (DL) techniques. This review comprehensively explores the evolution, methodologies, and applications of ML and DL in medical image classification. Traditional ML methods, including support vector machines and decision trees, have provided a foundation for early advancements by utilizing handcrafted features. However, the advent of DL, particularly convolutional neural networks (CNNs), has revolutionized the field by enabling automatic feature extraction and achieving superior performance. This review examines various DL architectures, such as ResNet, VGG, and Inception, highlighting their contributions to tasks like tumor detection, organ segmentation, and disease classification. Furthermore, it addresses challenges like data scarcity, interpretability, and computational demands, discussing potential solutions like data augmentation, transfer learning, and model optimization. The review also considers the ethical implications and the need for robust validation to ensure clinical applicability. Through a comparative analysis of existing studies, this review underscores the transformative impact of ML and DL on medical imaging, emphasizing the continuous need for innovation and interdisciplinary collaboration to enhance diagnostic accuracy and patient outcomes.

Section: Literature Studymentioning

confidence: 99%

Section: Literature Studymentioning

confidence: 99%

A Review on Machine Learning and Deep Learning Methods on Medical Image Classification

Degadwala,

Degadwala

2024

A Review on Rainfall Nowcasting Methodology using different ML and DL Models

Amit Solanki,

Dr. Sheshang Degadwala

2024

Weather is influenced by several factors such as temperature, pressure, air movement, moisture/water vapor, and the Earth's rotation. Forecasting weather with high geographic precision is both challenging and computationally demanding. This study explores a nowcasting method for meteorological radar images. Building on the concept of an unsupervised representation problem in deep learning, next-frame prediction is an emerging area in computer vision where future images are predicted based on previous ones. This has numerous applications, including in robotics and autonomous driving. The paper reviews the latest next-frame prediction networks, categorizing them into two main approaches: machine learning and deep learning. It compares these strategies by discussing their respective advantages and disadvantages. Lastly, the paper outlines the most promising directions for future research.

A Review on Identifying Lung Disease Sounds using different ML and DL Models

Jigisha Trivedi,

Dr. Sheshang Degadwala

2024

This comprehensive review explores the efficacy of various machine learning (ML) and deep learning (DL) models in identifying lung disease sounds, addressing the complex diagnostic challenges posed by the diverse acoustic patterns associated with lung diseases. ML algorithms like Support Vector Machines (SVM), Random Forests, and k-Nearest Neighbors (k-NN) offer robust classification frameworks, while DL architectures such as Convolutional Neural Networks (CNN) excel in extracting intricate audio patterns. By analyzing the performance metrics of these models, including accuracy, sensitivity, specificity, and area under the curve (AUC), the review aims to assess their comparative strengths and limitations in accurately identifying lung disease sounds. The insights gained from this review can significantly contribute to the development of more precise and effective diagnostic tools and interventions tailored to lung diseases, thus improving patient outcomes and healthcare efficiency in the realm of respiratory disorders.