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    Crossover based technique for data augmentation
    (Elsevier Ireland Ltd, 2022) Raj, R.; Mathew, J.; Kannath, S.K.; Rajan, J.
    Background and Objective: Medical image classification problems are frequently constrained by the availability of datasets. “Data augmentation” has come as a data enhancement and data enrichment solution to the challenge of limited data. Traditionally data augmentation techniques are based on linear and label preserving transformations; however, recent works have demonstrated that even non-linear, non-label preserving techniques can be unexpectedly effective. This paper proposes a non-linear data augmentation technique for the medical domain and explores its results. Methods: This paper introduces “Crossover technique”, a new data augmentation technique for Convolutional Neural Networks in Medical Image Classification problems. Our technique synthesizes a pair of samples by applying two-point crossover on the already available training dataset. By this technique, we create N new samples from N training samples. The proposed crossover based data augmentation technique, although non-label preserving, has performed significantly better in terms of increased accuracy and reduced loss for all the tested datasets over varied architectures. Results: The proposed method was tested on three publicly available medical datasets with various network architectures. For the mini-MIAS database of mammograms, our method improved the accuracy by 1.47%, achieving 80.15% using VGG-16 architecture. Our method works fine for both gray-scale as well as RGB images, as on the PH2 database for Skin Cancer, it improved the accuracy by 3.57%, achieving 85.71% using VGG-19 architecture. In addition, our technique improved accuracy on the brain tumor dataset by 0.40%, achieving 97.97% using VGG-16 architecture. Conclusion: The proposed novel crossover technique for training the Convolutional Neural Network (CNN) is painless to implement by applying two-point crossover on two images to form new images. The method would go a long way in tackling the challenges of limited datasets and problems of class imbalances in medical image analysis. Our code is available at https://github.com/rishiraj-cs/Crossover-augmentation © 2022
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    Generalizable DNN model for brain tumor sub-structure segmentation from low-resolution 2D multimodal MR Images
    (Elsevier Ltd, 2025) Bhaskaracharya, B.; Nair, R.P.; Prakashini, K.; R, G.M.; Litvak, P.; Mandava, P.; Vijayasenan, D.; Sumam David, S.D.
    Segmenting subregions within gliomas are critical for effective treatment planning of brain tumors. However, traditional methods of analyzing these regions using multiple MRI modalities are time-consuming, tedious, and subjective. To address these challenges, automatic segmentation models have been developed but are often built with complex 3D architecture using 3D MRI data. Also, brain tumor substructure segmentation is a highly class-imbalanced problem. To overcome these limitations, we propose two models that work on low-resolution 2D MRI data, widely used in resource-constrained countries. One model employs training a 2D U-NeT model using proposed hard sampling approach, demonstrating its effectiveness in segmenting gliomas, especially in datasets with extreme class imbalance. Another model incorporates pointwise and depthwise convolutions in each convolutional layer, enabling efficient information processing and feature learning. By ensembling the prediction maps of these models, we further improve overall segmentation performance. Our models were evaluated on the BraTS2018 dataset, achieving dice scores of 0.78 for Enhancing Tumor (ET), 0.82 for Tumor Core (TC), and 0.87 for Whole Tumor (WT). On a tertiary care hospital dataset, dice scores of 0.68 (ET), 0.75 (TC), and 0.84 (WT) were obtained, demonstrating their robustness and proximity to state-of-the-art methods. In summary, the proposed models offer efficient and reliable segmentation of glioma subregions. Their high dice scores, and computational efficiency, make them valuable tools for treatment planning and advancements in brain tumor segmentation. © 2024 Elsevier Ltd