Content-Based Medical Image Retrieval Using Pretrained Inception V3 Model

Rao, B. Ashwath; Kini, Gopalakrishna N; Nostas, Joshua

doi:10.1007/978-981-16-5747-4_55

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…As shown in Figure 6, Grid size reduction is typically accomplished through pooling processes [50]. To address the computational cost bottlenecks, a more efficient method is proposed.…”

Section: Grid Size Reductionmentioning

confidence: 99%

Explainable AI for binary and multi-class classification of leukemia using a modified transfer learning ensemble model

Deshpande,

Gite,

Pradhan

2024

International Journal on Smart Sensing and Intelligent Systems

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In leukemia diagnosis, automating the process of decision-making can reduce the impact of individual pathologists' expertise. While deep learning models have demonstrated promise in disease diagnosis, combining them can yield superior results. This research introduces an ensemble model that merges two pre-trained deep learning models, namely, VGG-16 and Inception, using transfer learning. It aims to accurately classify leukemia subtypes using real and standard dataset images, focusing on interpretability. Therefore, the use of Local Interpretable Model-Agnostic Explanations (LIME) is employed to achieve interpretability. The ensemble model achieves an accuracy of 83.33% in binary classification, outperforming individual models. In multi-class classification, VGG-16 and Inception reach accuracies of 83.335% and 93.33%, respectively, while the ensemble model reaches an accuracy of 100%.

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“…As shown in Figure 6, Grid size reduction is typically accomplished through pooling processes [50]. To address the computational cost bottlenecks, a more efficient method is proposed.…”

Section: Grid Size Reductionmentioning

confidence: 99%

Explainable AI for binary and multi-class classification of leukemia using a modified transfer learning ensemble model

Deshpande,

Gite,

Pradhan

2024

International Journal on Smart Sensing and Intelligent Systems

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show abstract

“…With transfer learning, InceptionV3 achieved high classification accuracy in numerous biomedical applications ( Ashwath Rao, Kini & Nostas, 2022 ; Al Husaini et al, 2022 ). The symmetric and asymmetric construction components of the InceptionV3 model include convolutions, average pooling, max pooling, concatenations, dropouts, and fully connected layers ( Szegedy et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Classification of breast cancer using a manta-ray foraging optimized transfer learning framework

Baghdadi

Malki

Balaha

et al. 2022

PeerJ Computer Science

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Due to its high prevalence and wide dissemination, breast cancer is a particularly dangerous disease. Breast cancer survival chances can be improved by early detection and diagnosis. For medical image analyzers, diagnosing is tough, time-consuming, routine, and repetitive. Medical image analysis could be a useful method for detecting such a disease. Recently, artificial intelligence technology has been utilized to help radiologists identify breast cancer more rapidly and reliably. Convolutional neural networks, among other technologies, are promising medical image recognition and classification tools. This study proposes a framework for automatic and reliable breast cancer classification based on histological and ultrasound data. The system is built on CNN and employs transfer learning technology and metaheuristic optimization. The Manta Ray Foraging Optimization (MRFO) approach is deployed to improve the framework’s adaptability. Using the Breast Cancer Dataset (two classes) and the Breast Ultrasound Dataset (three-classes), eight modern pre-trained CNN architectures are examined to apply the transfer learning technique. The framework uses MRFO to improve the performance of CNN architectures by optimizing their hyperparameters. Extensive experiments have recorded performance parameters, including accuracy, AUC, precision, F1-score, sensitivity, dice, recall, IoU, and cosine similarity. The proposed framework scored 97.73% on histopathological data and 99.01% on ultrasound data in terms of accuracy. The experimental results show that the proposed framework is superior to other state-of-the-art approaches in the literature review.

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Advanced CNN based on genetic algorithm to automated femoral neck fracture classification

Berrajaa,

Merras,

Berrajaa

2024

SIViP

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Content-Based Medical Image Retrieval Using Pretrained Inception V3 Model

Cited by 4 publications

References 18 publications

Explainable AI for binary and multi-class classification of leukemia using a modified transfer learning ensemble model

Explainable AI for binary and multi-class classification of leukemia using a modified transfer learning ensemble model

Classification of breast cancer using a manta-ray foraging optimized transfer learning framework

Advanced CNN based on genetic algorithm to automated femoral neck fracture classification

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