A comprehensive review of deep learning in lung cancer
- URL: http://arxiv.org/abs/2308.02528v1
- Date: Mon, 31 Jul 2023 16:28:42 GMT
- Title: A comprehensive review of deep learning in lung cancer
- Authors: Farzane Tajidini
- Abstract summary: We discuss the fundamentals of the area of cancer diagnosis, including the processes of cancer diagnosis and the standard classification methods employed by clinicians.
Current methods for cancer diagnosis are deemed ineffective, calling for new and more intelligent approaches.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: To provide the reader with a historical perspective on cancer classification
approaches, we first discuss the fundamentals of the area of cancer diagnosis
in this article, including the processes of cancer diagnosis and the standard
classification methods employed by clinicians. Current methods for cancer
diagnosis are deemed ineffective, calling for new and more intelligent
approaches.
Related papers
- 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) - Application analysis of ai technology combined with spiral CT scanning
in early lung cancer screening [15.6839495538166]
The overall 5-year survival rate of lung cancer patients is still lower than 20% and is staged.
In recent years, artificial intelligence technology has gradually begun to be applied in oncology.
This study applied the combined method in early lung cancer screening, aiming to find a safe and efficient screening mode.
arXiv Detail & Related papers (2024-01-26T07:58:09Z) - Deep Learning Techniques for Cervical Cancer Diagnosis based on
Pathology and Colposcopy Images [0.0]
Cervical cancer is a prevalent disease affecting millions of women worldwide every year.
Deep learning, a promising technology in computer vision, has emerged as a potential solution to improve the accuracy and efficiency of cervical cancer screening.
arXiv Detail & Related papers (2023-10-25T14:23:40Z) - Robust Tumor Detection from Coarse Annotations via Multi-Magnification
Ensembles [11.070094685209598]
We present a novel ensemble method that significantly improves the detection accuracy of metastasis on the open CAMELYON16 data set of sentinel lymph nodes of breast cancer patients.
Our experiments show that better results can be achieved with our technique making it clinically feasible to use for cancer diagnosis.
arXiv Detail & Related papers (2023-03-29T08:41:22Z) - 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) - Machine Learning Applications in Diagnosis, Treatment and Prognosis of
Lung Cancer [22.84388553607303]
We provide an overview of machine learning-based approaches that strengthen the varying aspects of lung cancer diagnosis and therapy.
We highlight the challenges and opportunities for future applications of machine learning in lung cancer.
arXiv Detail & Related papers (2022-03-05T17:43:57Z) - BI-RADS-Net: An Explainable Multitask Learning Approach for Cancer
Diagnosis in Breast Ultrasound Images [69.41441138140895]
This paper introduces BI-RADS-Net, a novel explainable deep learning approach for cancer detection in breast ultrasound images.
The proposed approach incorporates tasks for explaining and classifying breast tumors, by learning feature representations relevant to clinical diagnosis.
Explanations of the predictions (benign or malignant) are provided in terms of morphological features that are used by clinicians for diagnosis and reporting in medical practice.
arXiv Detail & Related papers (2021-10-05T19:14:46Z) - Early Diagnosis of Lung Cancer Using Computer Aided Detection via Lung
Segmentation Approach [0.1749935196721634]
American Cancer Society estimates about 27% of the deaths because of cancer.
In the early phase of its evolution, lung cancer does not cause any symptoms usually.
Many of the patients have been diagnosed in a developed phase where symptoms become more prominent, that results in poor curative treatment and high mortality rate.
arXiv Detail & Related papers (2021-07-23T05:46:06Z) - Topological Data Analysis of copy number alterations in cancer [70.85487611525896]
We explore the potential to capture information contained in cancer genomic information using a novel topology-based approach.
We find that this technique has the potential to extract meaningful low-dimensional representations in cancer somatic genetic data.
arXiv Detail & Related papers (2020-11-22T17:31:23Z) - CancerNet-SCa: Tailored Deep Neural Network Designs for Detection of
Skin Cancer from Dermoscopy Images [71.68436132514542]
Skin cancer continues to be the most frequently diagnosed form of cancer in the U.S.
In this study we introduce CancerNet-SCa, a suite of deep neural network designs tailored for the detection of skin cancer from dermoscopy images.
arXiv Detail & Related papers (2020-11-21T02:17:59Z) - Stan: Small tumor-aware network for breast ultrasound image segmentation [68.8204255655161]
We propose a novel deep learning architecture called Small Tumor-Aware Network (STAN) to improve the performance of segmenting tumors with different size.
The proposed approach outperformed the state-of-the-art approaches in segmenting small breast tumors.
arXiv Detail & Related papers (2020-02-03T22:25:01Z)
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.