Application of Deep Convulational Neural Network in Medical Image Classification

Yadav, Samir S.; Rathod, Rahul; Pawar, Sugat Ramrao; Pawar, Vaishali; More, Sitaram B.

doi:10.1109/esci50559.2021.9396854

Cited by 4 publications

(4 citation statements)

References 33 publications

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“…All the layers of this network help in the deeper classification of an image. Therefore, the classification accuracy of this network is much more higher and precise than any other model [24][25].…”

Section: Deep Learning Approachesmentioning

confidence: 92%

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Skin Disease Detection: Machine Learning vs Deep Learning

Bandyopadhyay¹,

Bhaumik²,

Poddar³

2021

Preprint

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Skin disease is a very common disease for humans. In the medical industry detecting skin disease and recognizing its type is a very challenging task. Due to the complexity of human skin texture and the visual closeness effect of the diseases, sometimes it is really difficult to detect the exact type. Therefore, it is necessary to detect and recognize the skin disease at its very first observation. In today's era, artificial intelligence (AI) is rapidly growing in medical fields. Different machine learning (ML) and deep learning(DL) algorithms are used for diagnostic purposes. These methods drastically improve the diagnosis process and also speed up the process. In this paper, a brief comparison between the machine learning process and the deep learning process was discussed. In both processes, three different and popular algorithms are used. For the machine Learning process Bagged Tree Ensemble, K-Nearest Neighbor (KNN), and Support Vector Machine(SVM) algorithms were used. For the deep learning process three pre-trained deep neural network models

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Section: Deep Learning Approachesmentioning

confidence: 92%

“…Deep Learning [20][21] is also a machine learning technique. It uses the architecture of the neural network that's why it's also known as a deep neural network.…”

Section: Deep Learning Approachesmentioning

confidence: 99%

Skin Disease Detection: Machine Learning vs Deep Learning

Bandyopadhyay¹,

Bhaumik²,

Poddar³

2021

Preprint

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show abstract

“…Cell image classification with CNNs presents unique challenges, particularly due to the high vari-ability in cell images and the often limited size of annotated datasets [26]. Cells can exhibit a wide range of appearances even within the same category, due to differences in staining, imaging conditions, and biological variability.…”

Section: B Non Temporal Approachmentioning

confidence: 99%

Insights into Cellular Evolution: Temporal Deep Learning Models and Analysis for Cell Image Classification

Zhao,

de Perez,

Dimitrova

et al. 2024

Preprint

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Understanding the temporal evolution of cells poses a significant challenge in developmental biology. This study embarks on a comparative analysis of various machine–learning techniques to classify sequences of cell colony images, thereby aiming to capture dynamic transitions of cellular states. Utilizing transfer learning with advanced classification networks, we achieved high accuracy in single–timestamp image categorization. We introduce temporal models—LSTM, R–Transformer, and ViViT—to explore the effectiveness of integrating temporal features in classification, comparing their performance against non-temporal models. This research benchmarks various machine learning approaches in understanding cellular dynamics, setting a foundation for future studies to enhance our understanding of cellular developments with computational methods, contributing significantly to biological research advancements.

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“…Nowadays, the medical image classification technology has made great progress. Yadav et al [1] made a summary of CNN used in medical image classification technology, and reached the conclusion that using deep CNN is more effective for medical image classification. Jeyaraj et al [2] used CNN to classify oral cancer, with an accuracy of 91% and reached the same conclusion.…”

Section: Introductionmentioning

confidence: 99%

Simplified Inception Module Based Hadamard Attention Mechanism for Medical Image Classification

Jin¹,

Zhang²,

Cai³

2023

JCC

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Medical image classification has played an important role in the medical field, and the related method based on deep learning has become an important and powerful technique in medical image classification. In this article, we propose a simplified inception module based Hadamard attention (SI + HA) mechanism for medical image classification. Specifically, we propose a new attention mechanism: Hadamard attention mechanism. It improves the accuracy of medical image classification without greatly increasing the complexity of the model. Meanwhile, we adopt a simplified inception module to improve the utilization of parameters. We use two medical image datasets to prove the superiority of our proposed method. In the BreakHis dataset, the AUCs of our method can reach 98.74%, 98.38%, 98.61% and 97.67% under the magnification factors of 40×, 100×, 200× and 400×, respectively. The accuracies can reach 95.67%, 94.17%, 94.53% and 94.12% under the magnification factors of 40×, 100×, 200× and 400×, respectively. In the KIMIA Path 960 dataset, the AUCs and accuracy of our method can reach 99.91% and 99.03%. It is superior to the currently popular methods and can significantly improve the effectiveness of medical image classification.

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Application of Deep Convulational Neural Network in Medical Image Classification

Cited by 4 publications

References 33 publications

Skin Disease Detection: Machine Learning vs Deep Learning

Skin Disease Detection: Machine Learning vs Deep Learning

Insights into Cellular Evolution: Temporal Deep Learning Models and Analysis for Cell Image Classification

Simplified Inception Module Based Hadamard Attention Mechanism for Medical Image Classification

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