Methods of Transfer Learning for Multiclass Hair Disease Categorization

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%

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

Degadwala,

Degadwala

2024

“…Degadwala et al [32] applied transfer learning for multiclass hair disease categorization, contributing to dermatological diagnostics. Degadwala et al [33] forecasted cancer death cases using supervised machine learning, offering predictive insights for healthcare planning.…”

Section: Introductionmentioning

confidence: 99%

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.

“…It involves analyzing fundamental concepts such as the bias-variance tradeoff, convergence properties, and the impact of different optimization techniques on model performance. By understanding these theoretical aspects, researchers and practitioners can better design, implement, and refine models to achieve optimal performance while mitigating issues like overfitting and underfitting [11][12][13][14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Theoretical Evaluation of Machine Learning and Deep Learning Applications in Various Domain

Dr. Sheshang Degadwala,

Dhairya Vyas

2024

The theoretical evaluation of machine learning (ML) and deep learning (DL) applications encompasses the rigorous analysis of their mathematical foundations, algorithmic principles, and performance metrics. This evaluation aims to understand the capabilities and limitations of various ML and DL models, including their generalization ability, convergence properties, and computational efficiency. By exploring theoretical aspects such as bias-variance tradeoff, overfitting, underfitting, and the impact of hyperparameters, researchers can optimize model architectures and training processes. Additionally, the theoretical examination provides insights into the interpretability and robustness of models, guiding the development of more reliable and efficient applications across diverse domains such as computer vision, natural language processing, and predictive analytics.