ANNCRIPS: Artificial Neural Networks for Cancer Research In Prediction & Survival

• URL: http://arxiv.org/abs/2309.15803v1
• Date: Tue, 26 Sep 2023 08:11:35 GMT
• Title: ANNCRIPS: Artificial Neural Networks for Cancer Research In Prediction & Survival
• Authors: Amit Mathapati
• Abstract summary: This study focuses on the development and validation of an intelligent mathematical model utilizing Artificial Neural Networks (ANNs) The model's implementation demonstrates promising potential in reducing the incidence of false positives, thereby improving patient outcomes. The long-term goal is to make this solution readily available for deployment in various screening centers, hospitals, and research institutions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Prostate cancer is a prevalent malignancy among men aged 50 and older. Current diagnostic methods primarily rely on blood tests, PSA:Prostate-Specific Antigen levels, and Digital Rectal Examinations (DRE). However, these methods suffer from a significant rate of false positive results. This study focuses on the development and validation of an intelligent mathematical model utilizing Artificial Neural Networks (ANNs) to enhance the early detection of prostate cancer. The primary objective of this research paper is to present a novel mathematical model designed to aid in the early detection of prostate cancer, facilitating prompt intervention by healthcare professionals. The model's implementation demonstrates promising potential in reducing the incidence of false positives, thereby improving patient outcomes. Furthermore, we envision that, with further refinement, extensive testing, and validation, this model can evolve into a robust, marketable solution for prostate cancer detection. The long-term goal is to make this solution readily available for deployment in various screening centers, hospitals, and research institutions, ultimately contributing to more effective cancer screening and patient care.

Related papers

• Enhancing Trust in Clinically Significant Prostate Cancer Prediction with Multiple Magnetic Resonance Imaging Modalities [61.36288157482697]
  In the United States, prostate cancer is the second leading cause of deaths in males with a predicted 35,250 deaths in 2024. In this paper, we investigate combining multiple MRI modalities to train a deep learning model to enhance trust in the models for clinically significant prostate cancer prediction.
  arXiv  Detail & Related papers  (2024-11-07T12:48:27Z)
• Enhancing Clinically Significant Prostate Cancer Prediction in T2-weighted Images through Transfer Learning from Breast Cancer [71.91773485443125]
  Transfer learning is a technique that leverages acquired features from a domain with richer data to enhance the performance of a domain with limited data. In this paper, we investigate the improvement of clinically significant prostate cancer prediction in T2-weighted images through transfer learning from breast cancer.
  arXiv  Detail & Related papers  (2024-05-13T15:57:27Z)
• 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)
• Cancer-Net PCa-Gen: Synthesis of Realistic Prostate Diffusion Weighted Imaging Data via Anatomic-Conditional Controlled Latent Diffusion [68.45407109385306]
  In Canada, prostate cancer is the most common form of cancer in men and accounted for 20% of new cancer cases for this demographic in 2022. There has been significant interest in the development of deep neural networks for prostate cancer diagnosis, prognosis, and treatment planning using diffusion weighted imaging (DWI) data. In this study, we explore the efficacy of latent diffusion for generating realistic prostate DWI data through the introduction of an anatomic-conditional controlled latent diffusion strategy.
  arXiv  Detail & Related papers  (2023-11-30T15:11:03Z)
• Comparative Analysis of Deep Learning Architectures for Breast Cancer Diagnosis Using the BreaKHis Dataset [0.0]
  In this study, we use and compare the performance of five well-known deep learning models for cancer classification. The results placed the Xception model at the top, with an F1 score of 0.9 and an accuracy of 89%. The F1 score for the Inception model was 87, while that for the InceptionResNet model was 86.
  arXiv  Detail & Related papers  (2023-09-02T19:02:50Z)
• Breast Cancer Detection and Diagnosis: A comparative study of state-of-the-arts deep learning architectures [3.883460584034766]
  The survival rates for breast cancer patients in certain third-world countries, like South Africa, are alarmingly low. Medical specialists and researchers have turned to domain-specific AI approaches, specifically deep learning models, to develop end-to-end solutions. This research focuses on evaluating the performance of various cutting-edge convolutional neural network (CNN) architectures in comparison to a relatively new model called the Vision Trans-former (ViT)
  arXiv  Detail & Related papers  (2023-05-31T15:21:34Z)
• 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)
• Deep learning methods for drug response prediction in cancer: predominant and emerging trends [50.281853616905416]
  Exploiting computational predictive models to study and treat cancer holds great promise in improving drug development and personalized design of treatment plans. A wave of recent papers demonstrates promising results in predicting cancer response to drug treatments while utilizing deep learning methods. This review allows to better understand the current state of the field and identify major challenges and promising solution paths.
  arXiv  Detail & Related papers  (2022-11-18T03:26:31Z)
• Enhancing Clinical Support for Breast Cancer with Deep Learning Models using Synthetic Correlated Diffusion Imaging [66.63200823918429]
  We investigate enhancing clinical support for breast cancer with deep learning models. We leverage a volumetric convolutional neural network to learn deep radiomic features from a pre-treatment cohort. We find that the proposed approach can achieve better performance for both grade and post-treatment response prediction.
  arXiv  Detail & Related papers  (2022-11-10T03:02:12Z)
• Artificial Neural Network Based Breast Cancer Screening: A Comprehensive Review [0.0]
  This paper provides a systematic review of the literature on artificial neural network based models for the diagnosis of breast cancer via mammography. The advantages and limitations of different ANN models including spiking neural network (SNN), deep belief network (DBN), convolutional neural network (CNN), multilayer neural network (MLNN), stacked autoencoders (SAE), and stacked de-noising autoencoders (SDAE) are described.
  arXiv  Detail & Related papers  (2020-05-29T17:13:51Z)

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.