[HTML][HTML] Recent advances in electrochemical biosensors: Applications, challenges, and future scope

A Singh, A Sharma, A Ahmed, AK Sundramoorthy… - Biosensors, 2021 - mdpi.com
The electrochemical biosensors are a class of biosensors which convert biological
information such as analyte concentration that is a biological recognition element …

Breast cancer detection using deep learning: Datasets, methods, and challenges ahead

RA Dar, M Rasool, A Assad - Computers in biology and medicine, 2022 - Elsevier
Breast Cancer (BC) is the most commonly diagnosed cancer and second leading cause of
mortality among women. About 1 in 8 US women (about 13%) will develop invasive BC …

Convolutional neural networks in medical image understanding: a survey

DR Sarvamangala, RV Kulkarni - Evolutionary intelligence, 2022 - Springer
Imaging techniques are used to capture anomalies of the human body. The captured images
must be understood for diagnosis, prognosis and treatment planning of the anomalies …

Advancing biosensors with machine learning

F Cui, Y Yue, Y Zhang, Z Zhang, HS Zhou - ACS sensors, 2020 - ACS Publications
Chemometrics play a critical role in biosensors-based detection, analysis, and diagnosis.
Nowadays, as a branch of artificial intelligence (AI), machine learning (ML) have achieved …

Deep and machine learning techniques for medical imaging-based breast cancer: A comprehensive review

EH Houssein, MM Emam, AA Ali… - Expert Systems with …, 2021 - Elsevier
Breast cancer is the second leading cause of death for women, so accurate early detection
can help decrease breast cancer mortality rates. Computer-aided detection allows …

Medical image segmentation using deep semantic-based methods: A review of techniques, applications and emerging trends

I Qureshi, J Yan, Q Abbas, K Shaheed, AB Riaz… - Information …, 2023 - Elsevier
Semantic-based segmentation (Semseg) methods play an essential part in medical imaging
analysis to improve the diagnostic process. In Semseg technique, every pixel of an image is …

Designing deep learning studies in cancer diagnostics

A Kleppe, OJ Skrede, S De Raedt, K Liestøl… - Nature Reviews …, 2021 - nature.com
The number of publications on deep learning for cancer diagnostics is rapidly increasing,
and systems are frequently claimed to perform comparable with or better than clinicians …

Artificial intelligence in radiology

A Hosny, C Parmar, J Quackenbush… - Nature Reviews …, 2018 - nature.com
Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated
remarkable progress in image-recognition tasks. Methods ranging from convolutional neural …

Deep learning in medical imaging and radiation therapy

B Sahiner, A Pezeshk, LM Hadjiiski, X Wang… - Medical …, 2019 - Wiley Online Library
The goals of this review paper on deep learning (DL) in medical imaging and radiation
therapy are to (a) summarize what has been achieved to date;(b) identify common and …

Cancer diagnosis using deep learning: a bibliographic review

K Munir, H Elahi, A Ayub, F Frezza, A Rizzi - Cancers, 2019 - mdpi.com
In this paper, we first describe the basics of the field of cancer diagnosis, which includes
steps of cancer diagnosis followed by the typical classification methods used by doctors …