scholarly journals Vibrational imaging for label-free cancer diagnosis and classification

2021 ◽  
Author(s):  
Renzo Vanna ◽  
Alejandro De la Cadena ◽  
Benedetta Talone ◽  
Cristian Manzoni ◽  
Marco Marangoni ◽  
...  
Keyword(s):  
Cancer Diagnosis ◽  
Label Free ◽  
Diagnosis And Classification

An Extensive Review on Machine Learning and Deep Learning Based Cervical Cancer Diagnosis and Classification Models

2020 ◽  
Vol 17 (12) ◽  
pp. 5438-5446
Author(s):  
C. Suguna ◽  
S. P. Balamurugan
Keyword(s):  
Machine Learning ◽  
Cervical Cancer ◽  
Deep Learning ◽  
Comparative Study ◽  
Cancer Diagnosis ◽  
Pap Smear ◽  
Classification Models ◽  
Extensive Review ◽  
Diagnosis And Classification ◽  
Automated Machine Learning

Cervical cancer is a commonly occurring deadliest disease among women, which needs earlier diagnosis to reduce the prevalence. Pap-smear is considered as a widely employed technique to screen and diagnose cervical cancer. Since classical manual screening techniques are inefficient in the identification of cervical cancer, several research works have been started to develop automated machine learning (ML) and deep learning (DL) tools for cervical cancer diagnosis. This paper surveys the recent works made on cervical cancer diagnosis and classification. The recently presently ML and DL models for cervical cancer diagnosis and classification has been reviewed in detail. Besides, segmentation techniques developed for cervical cancer diagnosis also surveyed. At the end of the survey, a brief comparative study has been carried out to identify the significance of the reviewed methods.

Review—A Review of Electrochemical Aptasensors for Label-Free Cancer Diagnosis

2020 ◽  
Vol 167 (6) ◽  
pp. 067511 ◽  
Author(s):  
Shahrzad Forouzanfar ◽  
Fahmida Alam ◽  
Nezih Pala ◽  
Chunlei Wang
Keyword(s):  
Cancer Diagnosis ◽  
Label Free

scholarly journals Label free technologies 3: infrared imaging applied to paraffinized tissue microarrays for colon cancer diagnosis

2013 ◽  
Vol 8 (S1) ◽  
Author(s):  
Ganesh D Sockalingum ◽  
Jayakrupakar Nallala ◽  
Marie-Danièle Diebold ◽  
Cyril Gobinet ◽  
Olivier Piot ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Cancer Diagnosis ◽  
Infrared Imaging ◽  
Tissue Microarrays ◽  
Label Free

Breast and endocrine surgery

2012 ◽  
Author(s):  
Matthew D. Gardiner ◽  
Neil R. Borley
Keyword(s):  
Cancer Diagnosis ◽  
Multiple Endocrine Neoplasia ◽  
Breast Development ◽  
Endocrine Surgery ◽  
Basic Principles ◽  
Thyroid State ◽  
Endocrine Neoplasia ◽  
Diagnosis And Classification ◽  
Altered Thyroid ◽  
Case Based

This chapter begins by discussing the basic principles of oncology, cancer diagnosis and classification, and cancer treatment, before focusing on the key areas of knowledge, namely disorders of breast development and involution, breast cancer assessment and management, goitre, altered thyroid state, thyroid cancer, parathyroid conditions, adrenal conditions, and multiple endocrine neoplasia. The chapter concludes with relevant case-based discussions.

Label‐free serum detection based on Raman spectroscopy for the diagnosis and classification of glioma

2020 ◽  
Vol 51 (10) ◽  
pp. 1977-1985
Author(s):  
Chenxi Zhang ◽  
Ying Han ◽  
Bo Sun ◽  
Wenli Zhang ◽  
Shujun Liu ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Label Free ◽  
Diagnosis And Classification ◽  
Free Serum ◽  
Serum Detection

scholarly journals Bioassays Based on Molecular Nanomechanics

2002 ◽  
Vol 18 (4) ◽  
pp. 167-174 ◽  
Author(s):  
Arun Majumdar
Keyword(s):  
Cancer Diagnosis ◽  
Protein Interactions ◽  
Dna Hybridization ◽  
Specific Antigen ◽  
Label Free ◽  
Receptor Ligand ◽  
Single Base ◽  
Ligand Interactions ◽  
Single Base Pair ◽  
Microcantilever Beam

Recent experiments have shown that when specific biomolecular interactions are confined to one surface of a microcantilever beam, changes in intermolecular nanomechanical forces provide sufficient differential torque to bend the cantilever beam. This has been used to detect single base pair mismatches during DNA hybridization, as well as prostate specific antigen (PSA) at concentrations and conditions that are clinically relevant for prostate cancer diagnosis. Since cantilever motion originates from free energy change induced by specific biomolecular binding, this technique is now offering a common platform for label-free quantitative analysis of protein-protein binding, DNA hybridization DNA-protein interactions, and in general receptor-ligand interactions. Current work is focused on developing “universal microarrays” of microcantilever beams for high-throughput multiplexed bioassays.

Machine Learning with Data Science-Enabled Lung Cancer Diagnosis and Classification Using Computed Tomography Images

2021 ◽  
Author(s):  
S. Vishwa Kiran ◽  
Inderjeet Kaur ◽  
K. Thangaraj ◽  
V. Saveetha ◽  
R. Kingsy Grace ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
Lung Cancer ◽  
Cancer Diagnosis ◽  
Data Science ◽  
Processing Technique ◽  
Ct Images ◽  
Lung Cancer Diagnosis ◽  
Computed Tomography Images ◽  
Diagnosis And Classification

In recent times, the healthcare industry has been generating a significant amount of data in distinct formats, such as electronic health records (EHR), clinical trials, genetic data, payments, scientific articles, wearables, and care management databases. Data science is useful for analysis (pattern recognition, hypothesis testing, risk valuation) and prediction. The major, primary usage of data science in the healthcare domain is in medical imaging. At the same time, lung cancer diagnosis has become a hot research topic, as automated disease detection poses numerous benefits. Although numerous approaches have existed in the literature for lung cancer diagnosis, the design of a novel model to automatically identify lung cancer is a challenging task. In this view, this paper designs an automated machine learning (ML) with data science-enabled lung cancer diagnosis and classification (MLDS-LCDC) using computed tomography (CT) images. The presented model initially employs Gaussian filtering (GF)-based pre-processing technique on the CT images collected from the lung cancer database. Besides, they are fed into the normalized cuts (Ncuts) technique where the nodule in the pre-processed image can be determined. Moreover, the oriented FAST and rotated BRIEF (ORB) technique is applied as a feature extractor. At last, sunflower optimization-based wavelet neural network (SFO-WNN) model is employed for the classification of lung cancer. In order to examine the diagnostic outcome of the MLDS-LCDC model, a set of experiments were carried out and the results are investigated in terms of different aspects. The resultant values demonstrated the effectiveness of the MLDS-LCDC model over the other state-of-the-art methods with the maximum sensitivity of 97.01%, specificity of 98.64%, and accuracy of 98.11%.

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