Deep Learning Enabled Segmentation, Classification and Risk Assessment of Cervical Cancer

• URL: http://arxiv.org/abs/2505.15505v1
• Date: Wed, 21 May 2025 13:25:27 GMT
• Title: Deep Learning Enabled Segmentation, Classification and Risk Assessment of Cervical Cancer
• Authors: Abdul Samad Shaik, Shashaank Mattur Aswatha, Rahul Jashvantbhai Pandya,
• Abstract summary: Cervical cancer, the fourth leading cause of cancer in women globally, requires early detection through Pap smear tests.<n>In this study, we performed a focused analysis by segmenting the cellular boundaries and drawing bounding boxes to isolate the cancer cells.<n>A novel Deep Learning architecture, the Multi-Resolution Fusion Deep Convolutional Network", was proposed to effectively handle images with varying resolutions and aspect ratios.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cervical cancer, the fourth leading cause of cancer in women globally, requires early detection through Pap smear tests to identify precancerous changes and prevent disease progression. In this study, we performed a focused analysis by segmenting the cellular boundaries and drawing bounding boxes to isolate the cancer cells. A novel Deep Learning (DL) architecture, the ``Multi-Resolution Fusion Deep Convolutional Network", was proposed to effectively handle images with varying resolutions and aspect ratios, with its efficacy showcased using the SIPaKMeD dataset. The performance of this DL model was observed to be similar to the state-of-the-art models, with accuracy variations of a mere 2\% to 3\%, achieved using just 1.7 million learnable parameters, which is approximately 85 times less than the VGG-19 model. Furthermore, we introduced a multi-task learning technique that simultaneously performs segmentation and classification tasks and begets an Intersection over Union score of 0.83 and a classification accuracy of 90\%. The final stage of the workflow employs a probabilistic approach for risk assessment, extracting feature vectors to predict the likelihood of normal cells progressing to malignant states, which can be utilized for the prognosis of cervical cancer.

Related papers

• Detection-Guided Deep Learning-Based Model with Spatial Regularization for Lung Nodule Segmentation [2.4044422838107438]
  Lung cancer ranks as one of the leading causes of cancer diagnosis and is the foremost cause of cancer-related mortality worldwide. The segmentation of lung nodules plays a critical role in aiding physicians in distinguishing between malignant and benign lesions. We introduce a novel model for segmenting lung nodules in computed tomography (CT) images, leveraging a deep learning framework that integrates segmentation and classification processes.
  arXiv  Detail & Related papers  (2024-10-26T11:58:12Z)
• Exploiting Precision Mapping and Component-Specific Feature Enhancement for Breast Cancer Segmentation and Identification [0.0]
  We propose novel Deep Learning (DL) frameworks for breast lesion segmentation and classification.<n>We introduce a precision mapping mechanism (PMM) for a precision mapping and attention-driven LinkNet (PMAD-LinkNet) segmentation framework.<n>We also introduce a component-specific feature enhancement module (CSFEM) for a component-specific feature-enhanced classifier (CSFEC-Net)
  arXiv  Detail & Related papers  (2024-07-03T06:40:26Z)
• AXIAL: Attention-based eXplainability for Interpretable Alzheimer's Localized Diagnosis using 2D CNNs on 3D MRI brain scans [43.06293430764841]
  This study presents an innovative method for Alzheimer's disease diagnosis using 3D MRI designed to enhance the explainability of model decisions. Our approach adopts a soft attention mechanism, enabling 2D CNNs to extract volumetric representations. With voxel-level precision, our method identified which specific areas are being paid attention to, identifying these predominant brain regions.
  arXiv  Detail & Related papers  (2024-07-02T16:44:00Z)
• Deep Learning-Based Segmentation of Tumors in PET/CT Volumes: Benchmark of Different Architectures and Training Strategies [0.12301374769426145]
  This study examines various neural network architectures and training strategies for automatically segmentation of cancer lesions. V-Net and nnU-Net models were the most effective for their respective datasets. Eliminating cancer-free cases from the AutoPET dataset was found to improve the performance of most models.
  arXiv  Detail & Related papers  (2024-04-15T13:03:42Z)
• Breast Cancer Image Classification Method Based on Deep Transfer Learning [40.392772795903795]
  A breast cancer image classification model algorithm combining deep learning and transfer learning is proposed. Experimental results demonstrate that the algorithm achieves an efficiency of over 84.0% in the test set, with a significantly improved classification accuracy compared to previous models.
  arXiv  Detail & Related papers  (2024-04-14T12:09:47Z)
• Developing a Novel Image Marker to Predict the Clinical Outcome of Neoadjuvant Chemotherapy (NACT) for Ovarian Cancer Patients [1.7623658472574557]
  Neoadjuvant chemotherapy (NACT) is one kind of treatment for advanced stage ovarian cancer patients. Partial responses to NACT may lead to suboptimal debulking surgery, which will result in adverse prognosis. We developed a novel image marker to achieve high accuracy prognosis prediction of NACT at an early stage.
  arXiv  Detail & Related papers  (2023-09-13T16:59:50Z)
• Prototypical Cross-domain Knowledge Transfer for Cervical Dysplasia Visual Inspection [37.66559952448587]
  We propose to leverage cross-domain cervical images that were collected in different but related clinical studies to improve the model's performance. To robustly learn the transferable information across datasets, we propose a novel prototype-based knowledge filtering method. Our proposed method outperforms the state-of-the-art cervical dysplasia visual inspection by an absolute improvement of 4.7% in top-1 accuracy.
  arXiv  Detail & Related papers  (2023-08-19T11:25:09Z)
• Domain Transfer Through Image-to-Image Translation for Uncertainty-Aware Prostate Cancer Classification [42.75911994044675]
  We present a novel approach for unpaired image-to-image translation of prostate MRIs and an uncertainty-aware training approach for classifying clinically significant PCa. Our approach involves a novel pipeline for translating unpaired 3.0T multi-parametric prostate MRIs to 1.5T, thereby augmenting the available training data. Our experiments demonstrate that the proposed method significantly improves the Area Under ROC Curve (AUC) by over 20% compared to the previous work.
  arXiv  Detail & Related papers  (2023-07-02T05:26:54Z)
• Deep learning-based approach to reveal tumor mutational burden status from whole slide images across multiple cancer types [41.61294299606317]
  Tumor mutational burden (TMB) is a potential genomic biomarker of immunotherapy. TMB detected through whole exome sequencing lacks clinical penetration in low-resource settings. In this study, we proposed a multi-scale deep learning framework to address the detection of TMB status from routinely used whole slide images.
  arXiv  Detail & Related papers  (2022-04-07T07:02:32Z)
• EMT-NET: Efficient multitask network for computer-aided diagnosis of breast cancer [58.720142291102135]
  We propose an efficient and light-weighted learning architecture to classify and segment breast tumors simultaneously. We incorporate a segmentation task into a tumor classification network, which makes the backbone network learn representations focused on tumor regions. The accuracy, sensitivity, and specificity of tumor classification is 88.6%, 94.1%, and 85.3%, respectively.
  arXiv  Detail & Related papers  (2022-01-13T05:24:40Z)
• Systematic Clinical Evaluation of A Deep Learning Method for Medical Image Segmentation: Radiosurgery Application [48.89674088331313]
  We systematically evaluate a Deep Learning (DL) method in a 3D medical image segmentation task. Our method is integrated into the radiosurgery treatment process and directly impacts the clinical workflow.
  arXiv  Detail & Related papers  (2021-08-21T16:15:40Z)
• Harvesting, Detecting, and Characterizing Liver Lesions from Large-scale Multi-phase CT Data via Deep Dynamic Texture Learning [24.633802585888812]
  We propose a fully-automated and multi-stage liver tumor characterization framework for dynamic contrast computed tomography (CT) Our system comprises four sequential processes of tumor proposal detection, tumor harvesting, primary tumor site selection, and deep texture-based tumor characterization.
  arXiv  Detail & Related papers  (2020-06-28T19:55:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.