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A comparative analysis of different augmentations for brain images

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Abstract

Deep learning (DL) requires a large amount of training data to improve performance and prevent overfitting. To overcome these difficulties, we need to increase the size of the training dataset. This can be done by augmentation on a small dataset. The augmentation approaches must enhance the model’s performance during the learning period. There are several types of transformations that can be applied to medical images. These transformations can be applied to the entire dataset or to a subset of the data, depending on the desired outcome. In this study, we categorize data augmentation methods into four groups: Absent augmentation, where no modifications are made; basic augmentation, which includes brightness and contrast adjustments; intermediate augmentation, encompassing a wider array of transformations like rotation, flipping, and shifting in addition to brightness and contrast adjustments; and advanced augmentation, where all transformation layers are employed. We plan to conduct a comprehensive analysis to determine which group performs best when applied to brain CT images. This evaluation aims to identify the augmentation group that produces the most favorable results in terms of improving model accuracy, minimizing diagnostic errors, and ensuring the robustness of the model in the context of brain CT image analysis.

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Data Availability

The dataset used in this study was obtained from the Brain Stroke CT Image Dataset on the Kaggle, which is a popular online community for data science and machine learning. The specific dataset used for brain stroke detection in this study consisted of CT images from various medical institutions.

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Authors and Affiliations

  1. Applied Sciences (Computer Applications), I.K. Gujral Punjab Technical University, Jalandhar, Kapurthala, India

    Shilpa Bajaj

  2. Department of Computer Science and Engineering, Khalsa College of Engineering and Technology, Amritsar, India

    Manju Bala

  3. Apex Institute of Technology (CSE), Chandigarh University, Gharuan, Mohali, Punjab, India

    Mohit Angurala

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Correspondence to Shilpa Bajaj.

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Bajaj, S., Bala, M. & Angurala, M. A comparative analysis of different augmentations for brain images. Med Biol Eng Comput 62, 3123–3150 (2024). https://doi.org/10.1007/s11517-024-03127-7

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