Multi-Class Classification of Vector Borne Diseases using Convolution Neural Network

Pandya, Darshanaben Dipakkumar; Patel, Shailesh Kantilal; Qureshi, Abdul Hamid; Goswami, Anil Kumar; Degadwala, Sheshang; Vyas, Dhairya

doi:10.1109/icaaic56838.2023.10140654

Cited by 14 publications

(1 citation statement)

References 15 publications

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“…Banerjee et al [31] develop a predictive model for diabetes management using patient data. Roy et al [32] apply machine learning techniques for fraud detection in insurance claims. Sinha et al [33] design AI-driven solutions for smart city infrastructure management.…”

Section: Literature Studymentioning

confidence: 99%

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

Degadwala,

Degadwala

2024

Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol

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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.

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Section: Literature Studymentioning

confidence: 99%

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

Degadwala,

Degadwala

2024

Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol

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Exploratory Data Analysis of Vector Borne Diseases using Random Forest, Gradient Boosting, AdaBoost, and XGBoost Pre-Trained Models

Gill,

Anand,

Gupta

2023

2023 IEEE Fifth International Conference on Advances in Electronics, Computers and Communications (ICAECC)

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

Amit Solanki,

Dr. Sheshang Degadwala

2024

Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol

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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.

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Multi-Class Classification of Vector Borne Diseases using Convolution Neural Network

Cited by 14 publications

References 15 publications

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

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

Exploratory Data Analysis of Vector Borne Diseases using Random Forest, Gradient Boosting, AdaBoost, and XGBoost Pre-Trained Models

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

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