Autokeras Approach: A Robust Automated Deep Learning Network for Diagnosis Disease Cases in Medical Images

Alaiad, Ahmad; Migdady, Aya; Al-Khatib, Ra’ed M.; AlZoubi, Omar; Zitar, Raed Abu; Abualigah, Laith

doi:10.3390/jimaging9030064

“…AutoKeras supports multiple types of data and tasks, including image classification, text classification, and regression problems, and continues to evolve and expand its capabilities to encompass a broader range of applications [14][15][16].…”

Section: B Autokerasmentioning

confidence: 99%

Classification and Regression Using Automatic Machine Learning (AutoML) – Open Source Code for Quick Adaptation and Comparison

Topsakal

¹

,

Akıncı

²

2023

Balkan Journal of Electrical and Computer Engineering

3

0

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This paper presents a comprehensive exploration of automatic machine learning (AutoML) tools in the context of classification and regression tasks. The focus lies on understanding and illustrating the potential of these tools to accelerate and optimize the process of machine learning, thereby making it more accessible to non-experts. Specifically, we delve into multiple popular open-source AutoML tools and provide illustrative examples of their application. We first discuss the fundamental principles of AutoML, including its key features such as automated data preprocessing, feature engineering, model selection, hyperparameter tuning, and model validation. We subsequently venture into the hands-on application of these tools, demonstrating the implementation of classification and regression tasks using multiple open-source AutoML tools. We provide open-source code samples for two data scenarios for classification and regression, designed to assist readers in quickly adapting AutoML tools for their own projects and in comparing the performance of different tools. We believe that this contribution will aid both practitioners and researchers in harnessing the power of AutoML for efficient and effective machine learning model development.

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“…Yang et al introduced MedMNIST 17 , a collection of ten pre-processed datasets of 28 × 28 medical images, and provided initial results from ResNet, Auto-sklearn, AutoKeras and Google AutoML Vision. AutoKeras has been applied to prostate cancer malignancy detection from multiparametric magnetic resonance images 18 , and also malaria detection from blood smear images 19 .…”

Section: Introductionmentioning

confidence: 99%

A comparative study of an on premise AutoML solution for medical image classification

Elangovan,

Lim,

Ting

2024

Sci Rep

0

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Automated machine learning (AutoML) allows for the simplified application of machine learning to real-world problems, by the implicit handling of necessary steps such as data pre-processing, feature engineering, model selection and hyperparameter optimization. This has encouraged its use in medical applications such as imaging. However, the impact of common parameter choices such as the number of trials allowed, and the resolution of the input images, has not been comprehensively explored in existing literature. We therefore benchmark AutoKeras (AK), an open-source AutoML framework, against several bespoke deep learning architectures, on five public medical datasets representing a wide range of imaging modalities. It was found that AK could outperform the bespoke models in general, although at the cost of increased training time. Moreover, our experiments suggest that a large number of trials and higher resolutions may not be necessary for optimal performance to be achieved.

show abstract

“…Yang et al introduced MedMNIST [13], a collection of ten preprocessed datasets of 28x28 medical images, and provided initial results from ResNet, Auto-sklearn, AutoKeras and Google AutoML Vision. AutoKeras has been applied to prostate cancer malignancy detection from multiparametric magnetic resonance images [14], and also malaria detection from blood smear images [15]. While a number of AutoML benchmarking studies for medical imaging have been performed as surveyed above, there may remain some gaps in the literature that we aim to ll in this study.…”

Section: Introductionmentioning

confidence: 99%

Medical Image Classification with On-Premise AutoML: Unveiling Insights through Comparative Analysis

Elangovan

¹

,

Lim

²

,

Ting

³

2023

Preprint

0

View full text Add to dashboard Cite

Automated machine learning (AutoML) allows for the simplified application of machine learning to real-world problems, by the implicit handling of necessary steps such as data pre-processing, feature engineering, model selection and hyperparameter optimization. This has encouraged its use in medical applications such as imaging. However, the impact of common parameter choices such as the number of trials allowed, and the resolution of the input images, has not been comprehensively explored in existing literature. We therefore benchmark AutoKeras (AK), an open-source AutoML framework, against several bespoke deep learning architectures, on five public medical datasets representing a wide range of imaging modalities. It was found that AK could outperform the bespoke models in general, although at the cost of increased training time. Moreover, our experiments suggest that a large number of trials and higher resolutions may not be necessary for optimal performance to be achieved.

show abstract

Autokeras Approach: A Robust Automated Deep Learning Network for Diagnosis Disease Cases in Medical Images

Cited by 10 publications

References 25 publications

Classification and Regression Using Automatic Machine Learning (AutoML) – Open Source Code for Quick Adaptation and Comparison

Classification and Regression Using Automatic Machine Learning (AutoML) – Open Source Code for Quick Adaptation and Comparison

A comparative study of an on premise AutoML solution for medical image classification

Medical Image Classification with On-Premise AutoML: Unveiling Insights through Comparative Analysis

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