[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 …
information such as analyte concentration that is a biological recognition element …
Breast cancer detection using deep learning: Datasets, methods, and challenges ahead
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 …
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 …
must be understood for diagnosis, prognosis and treatment planning of the anomalies …
Advancing biosensors with machine learning
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 …
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 …
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
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 …
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 …
and systems are frequently claimed to perform comparable with or better than clinicians …
Artificial intelligence in radiology
Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated
remarkable progress in image-recognition tasks. Methods ranging from convolutional neural …
remarkable progress in image-recognition tasks. Methods ranging from convolutional neural …
Deep learning in medical imaging and radiation therapy
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 …
therapy are to (a) summarize what has been achieved to date;(b) identify common and …
Cancer diagnosis using deep learning: a bibliographic review
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 …
steps of cancer diagnosis followed by the typical classification methods used by doctors …