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Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884

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Author: search.filters.author.Pulikala, A.Subject: search.filters.subject.Deep learning

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    AAPFC-BUSnet: Hierarchical encoder–decoder based CNN with attention aggregation pyramid feature clustering for breast ultrasound image lesion segmentation
    (Elsevier Ltd, 2024) Sushma, B.; Pulikala, A.
    Breast cancer causes a serious menace to women's health and lives, underscoring the urgency of accurate tumor detection. Detecting both cancerous and non-cancerous breast tumors has become increasingly crucial, with ultrasound imaging emerging as a widely adopted modality for this purpose. However, identifying breast lesions in ultrasound images is a challenging task due to various tumor morphologies, geometry, similar color intensity distributions, and fuzzy boundaries, particularly irregularly shaped malignant tumors. This work proposes an encoder–decoder based U-shaped convolutional neural network (CNN) variant with an attention aggregation-based pyramid feature clustering module (AAPFC) to detect breast lesion regions. The network consists of the U-Net variant as a base network and AAPFC to fuse features extracted at the various levels of the base U-Net using a suitable feature fusion technique. Furthermore, the deformable convolution with adaptive self-attention mechanism is introduced to decode the pyramid features parallel to capture the various geometric features at multi-stages. Two public breast lesion ultrasound datasets consisting 263 malignant, 547 benign and 133 normal images are considered to evaluate the performance of the proposed model and state-of-the-art deep CNN-based segmentation models. The proposed model provides 96% accuracy, 68% Mean-IoU, 97% specificity, 82% sensitivity and 0.747 kappa score respectively. The conducted qualitative and quantitative performance analysis experiments show that the proposed model performs better in breast lesion segmentation on ultrasound images. © 2024 Elsevier Ltd
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    Deep Speech Denoising with Minimal Dependence on Clean Speech Data
    (Birkhauser, 2024) Poluboina, V.; Pulikala, A.; Pitchaimuthu, A.N.
    Most of the existing deep learning-based speech denoising methods rely heavily on clean speech data. According to the traditional view, a large number of noisy and clean speech samples are required for good speech denoising performance. However, the data collection is a technical barrier to this criteria, particularly in economically challenged areas and for languages with limited resources. Training deep denoising networks with only noisy speech samples is a viable option to avoid dependence on sample data size. In this study, the target and input of a DCU-Net were trained using only noisy speech samples. Experimental results demonstrate that, when compared to traditional speech denoising techniques, the proposed approach avoids not only the high dependence on clean targets but also the high dependence on large data sizes. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.