Hybrid deep convolution model for lung cancer detection with transfer learning

• URL: http://arxiv.org/abs/2501.02785v1
• Date: Mon, 06 Jan 2025 06:01:01 GMT
• Title: Hybrid deep convolution model for lung cancer detection with transfer learning
• Authors: Sugandha Saxena, S. N. Prasad, Ashwin M Polnaya, Shweta Agarwala,
• Abstract summary: Lung cancer remains one of the leading causes of cancer-related mortality worldwide. We introduce a hybrid deep convolution model leveraging transfer learning, named the Maximum Sensitivity Neural Network (MSNN) MSNN is designed to improve the precision of lung cancer detection by refining sensitivity and specificity.
• Score: 0.0
• License:
• Abstract: Advances in healthcare research have significantly enhanced our understanding of disease mechanisms, diagnostic precision, and therapeutic options. Yet, lung cancer remains one of the leading causes of cancer-related mortality worldwide due to challenges in early and accurate diagnosis. While current lung cancer detection models show promise, there is considerable potential for further improving the accuracy for timely intervention. To address this challenge, we introduce a hybrid deep convolution model leveraging transfer learning, named the Maximum Sensitivity Neural Network (MSNN). MSNN is designed to improve the precision of lung cancer detection by refining sensitivity and specificity. This model has surpassed existing deep learning approaches through experimental validation, achieving an accuracy of 98% and a sensitivity of 97%. By overlaying sensitivity maps onto lung Computed Tomography (CT) scans, it enables the visualization of regions most indicative of malignant or benign classifications. This innovative method demonstrates exceptional performance in distinguishing lung cancer with minimal false positives, thereby enhancing the accuracy of medical diagnoses.

Related papers

• Advanced Lung Nodule Segmentation and Classification for Early Detection of Lung Cancer using SAM and Transfer Learning [0.0]
  This study introduces an innovative approach to lung nodule segmentation by utilizing the Segment Anything Model (SAM) combined with transfer learning techniques. The proposed method leverages Bounding Box prompts and a vision transformer model to enhance segmentation performance, achieving high accuracy, Dice Similarity Coefficient (DSC) and Intersection over Union (IoU) metrics. The findings demonstrate the proposed model effectiveness in precisely segmenting lung nodules from CT scans, underscoring its potential to advance early detection and improve patient care outcomes in lung cancer diagnosis.
  arXiv  Detail & Related papers  (2024-12-31T18:21:57Z)
• Medical AI for Early Detection of Lung Cancer: A Survey [11.90341994990241]
  Lung cancer remains one of the leading causes of morbidity and mortality worldwide. Computer-aided diagnosis (CAD) systems have proven effective in detecting and classifying pulmonary nodules. Deep learning algorithms have markedly improved the accuracy and efficiency of pulmonary nodule analysis.
  arXiv  Detail & Related papers  (2024-10-18T17:45:42Z)
• Boosting Medical Image-based Cancer Detection via Text-guided Supervision from Reports [68.39938936308023]
  We propose a novel text-guided learning method to achieve highly accurate cancer detection results. Our approach can leverage clinical knowledge by large-scale pre-trained VLM to enhance generalization ability.
  arXiv  Detail & Related papers  (2024-05-23T07:03:38Z)
• Improving Breast Cancer Grade Prediction with Multiparametric MRI Created Using Optimized Synthetic Correlated Diffusion Imaging [71.91773485443125]
  Grading plays a vital role in breast cancer treatment planning. The current tumor grading method involves extracting tissue from patients, leading to stress, discomfort, and high medical costs. This paper examines using optimized CDI$s$ to improve breast cancer grade prediction.
  arXiv  Detail & Related papers  (2024-05-13T15:48:26Z)
• Using Multiparametric MRI with Optimized Synthetic Correlated Diffusion Imaging to Enhance Breast Cancer Pathologic Complete Response Prediction [71.91773485443125]
  Neoadjuvant chemotherapy has recently gained popularity as a promising treatment strategy for breast cancer. The current process to recommend neoadjuvant chemotherapy relies on the subjective evaluation of medical experts. This research investigates the application of optimized CDI$s$ to enhance breast cancer pathologic complete response prediction.
  arXiv  Detail & Related papers  (2024-05-13T15:40:56Z)
• Leveraging object detection for the identification of lung cancer [0.15229257192293202]
  The YOLOv5 model was employed to train an algorithm capable of detecting cancerous lung lesions. The trained YOLOv5 model exhibited exceptional proficiency in identifying lung cancer lesions, displaying high accuracy and recall rates.
  arXiv  Detail & Related papers  (2023-05-25T07:53:18Z)
• 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)
• Automatic Generation of Interpretable Lung Cancer Scoring Models from Chest X-Ray Images [9.525711971667679]
  Lung cancer is the leading cause of cancer death worldwide. Deep learning techniques are effective at automatically diagnosing lung cancer. These techniques have yet to be clinically approved and adopted by the medical community.
  arXiv  Detail & Related papers  (2020-12-10T04:11:59Z)
• 3D Neural Network for Lung Cancer Risk Prediction on CT Volumes [0.6810862244331126]
  Lung cancer is the most common cause of cancer death in the United States. Lung cancer CT screening has been shown to reduce mortality by up to 40% and is now included in US screening guidelines. Despite the use of standards for radiological diagnosis, persistent inter-grader variability and incomplete characterization of comprehensive imaging findings remain as limitations of current methods. In this report, we reproduce a state-of-the-art deep learning algorithm for lung cancer risk prediction.
  arXiv  Detail & Related papers  (2020-07-25T10:01:22Z)
• Deep Learning for Automatic Pneumonia Detection [72.55423549641714]
  Pneumonia is the leading cause of death among young children and one of the top mortality causes worldwide. Computer-aided diagnosis systems showed the potential for improving diagnostic accuracy. We develop the computational approach for pneumonia regions detection based on single-shot detectors, squeeze-and-excitation deep convolution neural networks, augmentations and multi-task learning.
  arXiv  Detail & Related papers  (2020-05-28T10:54:34Z)
• Spatio-spectral deep learning methods for in-vivo hyperspectral laryngeal cancer detection [49.32653090178743]
  Early detection of head and neck tumors is crucial for patient survival. Hyperspectral imaging (HSI) can be used for non-invasive detection of head and neck tumors. We present multiple deep learning techniques for in-vivo laryngeal cancer detection based on HSI.
  arXiv  Detail & Related papers  (2020-04-21T17:07:18Z)

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.