Role of Machine Learning and Texture Features for the Diagnosis of Laryngeal Cancer

Singh, Vibhav Prakash; Maurya, Ashish Kumar

doi:10.1002/9781119792611.ch23

Cited by 7 publications

(3 citation statements)

References 18 publications

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“…In 2021, Scholar [5] proposed a deep learning-based breast cancer detection framework that utilizes transfer learning techniques to extract deep features of breast tissue images through a pre-trained neural network model, which is then combined with patient clinical information to enhance the predictive accuracy of the model. This work shows that combining medical image data with patients' clinical data can effectively improve the accuracy of breast cancer diagnosis.…”

Section: Latest Research Progressmentioning

confidence: 99%

Design and implementation of breast cancer prediction system based on machine learning

Shengjie

2024

TNS

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With the increasing incidence of breast cancer worldwide, early diagnosis and treatment of breast cancer has become the key to improving patient survival and quality of life. As a powerful data analysis tool, machine learning is increasingly widely used in the medical field, especially in disease prediction and assisted diagnosis. This paper aims to design and implement a machine learning-based breast cancer prediction system to improve the early diagnosis rate of breast cancer and reduce medical costs. Through an in-depth analysis of the global incidence of breast cancer and the current application of machine learning in the medical field, this study clarified the importance of breast cancer prediction and the problems existing in the existing prediction system. This paper further discusses the theoretical basis of machine learning in breast cancer detection, evaluates the advantages and disadvantages of commonly used machine learning algorithms, and reviews the latest research progress in this field at home and abroad. In the part of system design and implementation, the architecture design, data flow and processing process of the prediction system, as well as the method of data preprocessing and feature selection are introduced in detail. In addition, this paper also constructs a machine learning model suitable for breast cancer prediction, and carries out systematic implementation and testing through the actual development environment. In the discussion section, the applicability of machine learning model in breast cancer prediction is analyzed, the causes of model inefficiency are discussed, and the corresponding solutions are proposed. Finally, the paper summarizes the content of the full text, points out the limitations of the research, and puts forward the direction of future research. The results of this study not only provide a new technical means for the early diagnosis of breast cancer, but also provide valuable experience for the application of machine learning in the medical field.

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Section: Latest Research Progressmentioning

confidence: 99%

Design and implementation of breast cancer prediction system based on machine learning

Shengjie

2024

TNS

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“…Analyzed the Raman spectra from normal larynxes and cancerous larynxes and used a feature extraction approach to identify which parts of the Raman spectra indicated presence of a tumor [18]. Finally, a study by Singh and Maurya proposed a computer-aided-diagnosis system which can analyze patches of endoscopic videos, perform feature extraction, and determine which parts of an endoscopy are most crucial for diagnosis [19]. However, there is no existing research that uses biological attributes of microRNA for laryngeal cancer diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Laryngeal Cancer Diagnosis via miRNA-based Decision Tree Model

Arora,

Tsigelny,

Kouznetsova

2023

Preprint

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Purpose Laryngeal cancer (LC) is the most common head and neck cancer, which often goes undiagnosed due to the expensiveness and inaccessible nature of current diagnosis methods. Many recent studies have shown that microRNAs (miRNAs) are crucial biomarkers for a variety of cancers. Methods In this study, we create a decision tree model for the diagnosis of laryngeal cancer using a calculated miRNAs’ attributes, such as sequence-based characteristics, predicted miRNA target genes, and gene pathways. This series of attributes is extracted from both differentially expressed blood-based miRNAs in laryngeal cancer and random, non-associated with cancer miRNAs. Results Several machine-learning (ML) algorithms were tested in the ML model, and the Hoeffding Tree (HT) classifier yields the highest accuracy (86.8%) in miRNAs-based recognition of laryngeal cancer. Furthermore, HT-based model is validated with the independent laryngeal cancer datasets and can accurately diagnose laryngeal cancer with 86% accuracy. We also explored the biological relationships of the attributes used in HT-based model to understand their relationship with cancer proliferation or suppression pathways. Conclusion Our study demonstrates that the proposed model and an inexpensive miRNA testing strategy have the potential to serve as a cost-effective and accessible method for diagnosing laryngeal cancer.

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“…In this regard, several computer-based approaches were used on the larynx endoscopic images. These methods can assist otolaryngologists by providing complementary information regarding the stage of the cancer and characteristics of the vascular trees and larynx epithelial tissue [11]. In the area of laryngoscopic and NBI image analysis, an ensemble of Convolutional Neural Networks (CNN) with texture and frequency-domain-based features [12] and a set of hand-crafted texture and first-order statistical features [13] were proposed for larynx cancerous tissue classification.…”

Section: Introductionmentioning

confidence: 99%

Deep Convolution Neural Network for Laryngeal Cancer Classification on Contact Endoscopy-Narrow Band Imaging

Esmaeili

Sharaf

Ataide

et al. 2021

Sensors

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(1) Background: Contact Endoscopy (CE) and Narrow Band Imaging (NBI) are optical imaging modalities that can provide enhanced and magnified visualization of the superficial vascular networks in the laryngeal mucosa. The similarity of vascular structures between benign and malignant lesions causes a challenge in the visual assessment of CE-NBI images. The main objective of this study is to use Deep Convolutional Neural Networks (DCNN) for the automatic classification of CE-NBI images into benign and malignant groups with minimal human intervention. (2) Methods: A pretrained Res-Net50 model combined with the cut-off-layer technique was selected as the DCNN architecture. A dataset of 8181 CE-NBI images was used during the fine-tuning process in three experiments where several models were generated and validated. The accuracy, sensitivity, and specificity were calculated as the performance metrics in each validation and testing scenario. (3) Results: Out of a total of 72 trained and tested models in all experiments, Model 5 showed high performance. This model is considerably smaller than the full ResNet50 architecture and achieved the testing accuracy of 0.835 on the unseen data during the last experiment. (4) Conclusion: The proposed fine-tuned ResNet50 model showed a high performance to classify CE-NBI images into the benign and malignant groups and has the potential to be part of an assisted system for automatic laryngeal cancer detection.

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Role of Machine Learning and Texture Features for the Diagnosis of Laryngeal Cancer

Cited by 7 publications

References 18 publications

Design and implementation of breast cancer prediction system based on machine learning

Design and implementation of breast cancer prediction system based on machine learning

Laryngeal Cancer Diagnosis via miRNA-based Decision Tree Model

Deep Convolution Neural Network for Laryngeal Cancer Classification on Contact Endoscopy-Narrow Band Imaging

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