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All Journal International Journal of Advances in Intelligent Informatics Applied Engineering and Technology
Mukku, Lalasa
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Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology

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Journal : International Journal of Advances in Intelligent Informatics

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CMT-CNN: colposcopic multimodal temporal hybrid deep learning model to detect cervical intraepithelial neoplasia Mukku, Lalasa; Thomas, Jyothi
International Journal of Advances in Intelligent Informatics Vol 10, No 2 (2024): May 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v10i2.1527

Abstract

Cervical cancer poses a significant threat to women's health in developing countries, necessitating effective early detection methods. In this study, we introduce the Colposcopic Multimodal Temporal Convolution Neural Network (CMT-CNN), a novel model designed for classifying cervical intraepithelial neoplasia by leveraging sequential colposcope images and integrating extracted features with clinical data. Our approach incorporates Mask R-CNN for precise cervix region segmentation and deploys the EfficientNet B7 architecture to extract features from saline, iodine, and acetic acid images. The fusion of clinical data at the decision level, coupled with Atrous Spatial Pyramid Pooling-based classification, yields remarkable results: an accuracy of 92.31%, precision of 90.19%, recall of 89.63%, and an F-1 score of 90.72. This achievement not only establishes the superiority of the CMT-CNN model over baselines but also paves the way for future research endeavours aiming to harness heterogeneous data types in the development of deep learning models for cervical cancer screening. The implications of this work are profound, offering a potent tool for early cervical cancer detection that combines multimodal data and clinical insights, potentially saving countless lives.
TelsNet: temporal lesion network embedding in a transformer model to detect cervical cancer through colposcope images Mukku, Lalasa; Thomas, Jyothi
International Journal of Advances in Intelligent Informatics Vol 9, No 3 (2023): November 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v9i3.1431

Abstract

Cervical cancer ranks as the fourth most prevalent malignancy among women globally. Timely identification and intervention in cases of cervical cancer hold the potential for achieving complete remission and cure. In this study, we built a deep learning model based on self-attention mechanism using transformer architecture to classify the cervix images to help in diagnosis of cervical cancer. We have used techniques like an enhanced multivariate gaussian mixture model optimized with mexican axolotl algorithm for segmenting the colposcope images prior to the Temporal Lesion Convolution Neural Network (TelsNet) classifying the images. TelsNet is a transformer-based neural network that uses temporal convolutional neural networks to identify cancerous regions in colposcope images. Our experiments show that TelsNet achieved an accuracy of 92.7%, with a sensitivity of 73.4% and a specificity of 82.1%. We compared the performance of our model with various state-of-the-art methods, and our results demonstrate that TelsNet outperformed the other methods. The findings have the potential to significantly simplify the process of detecting and accurately classifying cervical cancers at an early stage, leading to improved rates of remission and better overall outcomes for patients globally.
Precise cervical cancer cell boundary denoising and segmentation with adaptive wavelet-spectral enhancement Mukku, Lalasa; Laman, Manjunath Ramanna; Hegde, Lavanya; Mahapurush, Prathima; Mahapurush, Shivanandaswamy
International Journal of Advances in Intelligent Informatics Vol 12, No 1 (2026): February 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i1.2267

Abstract

Accurate segmentation of cell nuclei in cervical cytology images is crucial for automated cervical cancer screening, yet existing methods struggle with blurred boundaries, noise-induced degradation, and topologically implausible predictions. The current research proposes Cell-Seg Tool, a novel triplet-branch diffusion AI tool that synergistically integrates three innovations to address these limitations. The Wavelet-Enhanced Contour Refinement Branch employs a learnable multi-scale discrete wavelet transform with adaptive coefficient attention to dynamically enhance boundary features across horizontal, vertical, and diagonal orientations. The Adaptive Spectral Noise Suppression module performs dual-domain processing using DCT-based filtering and uncertainty-guided fusion, coupled with bidirectional anchor semantic feedback to couple cross-branch information. The Topology-Aware Hybrid Loss integrates a focal Tversky loss, a persistent homology loss, a directional boundary loss, a skeleton completeness loss, and a diffusion-noise MSE loss for multi-objective optimization. Comprehensive experiments on multiple datasets demonstrate superior performance, achieving 94.45% Dice coefficient and 19.2% reduction in boundary localization error compared to state-of-the-art methods. Unlike prior work that applies these techniques independently, this work demonstrates that their adaptive, synergistic integration within a diffusion-based framework yields substantial improvements in boundary accuracy and topological correctness.
Co-Authors Hegde, Lavanya Laman, Manjunath Ramanna Mahapurush, Prathima Mahapurush, Shivanandaswamy Thomas, Jyothi