R-GDORUS technology: Effectively solving the Raman spectral data imbalance in medical diagnosis

Chen, Chen; Wu, Xue; Zuo, Enguang; Chen, Cheng; Lv, Xiaoyi; Wu, Lei

doi:10.1016/j.chemolab.2023.104762

Cited by 6 publications

(2 citation statements)

References 34 publications

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“…The proposed context Aggregator federated learning model was tested on the COVID-19 imaging dataset and gave better results than the standard federating average learning algorithms. To handle the imbalanced data in Raman spectroscopy, [22] proposed a hybrid sampling method of Raman-Gaussian distributed oversampling attached with random undersampling. The proposed method was applied to the dataset of malignant tumors, class B infectious diseases, and autoimmune diseases.…”

Section: Related Workmentioning

confidence: 99%

A Unified Approach Addressing Class Imbalance in Magnetic Resonance Image for Deep Learning Models

Cui,

Li,

Yang

et al. 2024

IEEE Access

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Medical image datasets, particularly those comprising Magnetic Resonance (MR) images, are essential for accurate diagnosis and treatment planning. However, these datasets often suffer from class imbalance, where certain classes of abnormalities have unequal representation. Models trained on imbalanced datasets can be biased towards the prominent class, leading to misclassification. Addressing class imbalance problems is crucial to developing robust deep-learning MR image analysis models. This research focuses on the class imbalance problem in MR image datasets and proposes a novel approach to enhance deep learning models. We have introduced a unified approach equipped with a selective attention mechanism, unified loss function, and progressive resizing. The selective attention strategy identifies prominent regions within the underlying image to find the feature maps, retaining only the relevant activations of the minority class. Finetuning of the multiple hyperparameters was achieved using a novel unified loss function that plays a vital role in enhancing the overwhelming error performance for minority classes and accuracy for common classes. To address the class imbalances phenomenon, we incorporate progressive resizing that can dynamically adjust the input image size as the model trains. This dynamic nature helps handle class imbalances and improve overall performance. The research evaluates the proposed approach's effectiveness by embedding it into five state-of-the-art CNN models: UNet, FCN, RCNN, SegNet, and Deeplab-V3. For experimental purposes, we have selected five diverse MR image datasets, BUS2017, MICCAI 2015 head and neck, ATLAS, BRATS 2015, and Digital Database Thyroid Image (DDTI), to evaluate the performance of the proposed approach against state-of-the-art techniques. The assessment of the proposed approach reveals improved performance across all metrics for five different MR imaging datasets. DeepLab-V3 demonstrated the best performance, achieving IoU, DSC, Precision, and Recall scores of 0.893, 0.953, 0.943, and 0.944, respectively, on the BUS dataset. These scores indicate an improvement of 5% in DSC, 6% in IoU, 4% in precision, and approximately 4% in recall compared to the baseline. The most significant increases were observed in the ATLAS and LiTS MICCAI 2017 datasets, with a 5% and 7% increase in IoU and DSC over the baseline (DSC = 0.628, DSC = 0.695) for the ATLAS dataset, and a 5% and 9% increase in IoU and DSC for the LiTS MICCAI 2017 dataset.

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Section: Related Workmentioning

confidence: 99%

A Unified Approach Addressing Class Imbalance in Magnetic Resonance Image for Deep Learning Models

Cui,

Li,

Yang

et al. 2024

IEEE Access

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“…This poses a difficulty for learning algorithms, as they will be biased toward the majority group. [12][13][14] At the same time, the minority class is the item with more significance from the data mining perspective, and despite its rareness, it may carry important and useful knowledge. [15] Surface-enhanced Raman spectroscopy (SERS) has shown highly promising for existing cancer screening.…”

Section: Introductionmentioning

confidence: 99%

Power‐Law‐Based Synthetic Minority Oversampling Technique on Imbalanced Serum Surface‐Enhanced Raman Spectroscopy Data for Cancer Screening

Pan

Peng

Chen

et al. 2023

Advanced Intelligent Systems

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Surface‐enhanced Raman spectroscopy (SERS) has shown highly promising for existing cancer screening. However, previous “proof‐of‐concept” studies ignored the natural imbalance of cancer types in the population, leading the model to be biased toward learning more features in majority class during the learning process at the expense of ignoring minority class. Herein, a power‐law‐based synthetic minority oversampling technique (PL‐SMOTE) method is proposed to guide the resampling of multiclass serum SERS data by analyzing the long‐tailed (power‐law) distribution of cancer prevalence in the population. The proposed PL‐SMOTE method balances the number of minorities to resample and the number of overlaps between classes by introducing modulating factor. Modeling on resampled datasets synthesized by PL‐SMOTE verifies the effectiveness of proposed PL‐SMOTE method. After further fine‐tuning, the parameters of the deep neural network model and PL‐SMOTE method, an optimal cancer screening model with an optimal macroaveraged Recall score of 97.24% and an optimal macroaveraged F2‐Score of 97.38% is obtained. A new method for multiclass imbalanced resampling is provided, which has significant improvement on model performance in terms of SERS cancer screening. The method also inspires in other multiclass imbalanced scenario, such as biological medicine, abnormal detection, and disaster prediction.

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Class-overlap detection based on heterogeneous clustering ensemble for multi-class imbalance problem

Dai,

Wang,

et al. 2024

Expert Systems with Applications

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R-GDORUS technology: Effectively solving the Raman spectral data imbalance in medical diagnosis

Cited by 6 publications

References 34 publications

A Unified Approach Addressing Class Imbalance in Magnetic Resonance Image for Deep Learning Models

A Unified Approach Addressing Class Imbalance in Magnetic Resonance Image for Deep Learning Models

Power‐Law‐Based Synthetic Minority Oversampling Technique on Imbalanced Serum Surface‐Enhanced Raman Spectroscopy Data for Cancer Screening

Class-overlap detection based on heterogeneous clustering ensemble for multi-class imbalance problem

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