Current advancements in machine learning, particularly deep
learning, are proving to be useful in identifying and quantifying patterns in
medical pictures. The crucial power of deep learning algorithms to generate
hierarchical feature representations directly from pictures, which eliminates
the need for handcrafted features, lies at the heart of these breakthroughs.
Deep learning is quickly becoming the state-of-the-art for medical image
processing, with performance gains in a wide range of clinical applications.
We want to develop a highly automated technique for identifying and staging
precancerous and cervical cancers, as well as thyroid malignancies, that is
improved using deep learning (DL) and confirmed by a randomised controlled
clinical trial. We wish to use U-Net-based architectures to build a
high-performance deep learning model for medical image segmentation that is
independent of organs/tissues, dimensions, or image type (2D/3D) and verify it
in a randomised, controlled clinical trial. Based on the U-Net-based
architectures that we envisaged, we mostly used U-Net-based architecture
analysis to identify the primary aspects that we considered were important in
the design and optimization of the integrated DL model. Second, we'll test the
DL model's performance in a randomised, controlled clinical study. Precancers,
cervical cancer, and thyroid cancer will all be diagnosed and staged using our
DL model, which will be highly automated. We created a composite model that
takes into consideration the major elements of each architecture. Attention
gate mechanism is an improvement added to convolutional network architecture
for fast and precise image segmentation (Attention U-Net), Harmony Densely
Connected Network-Medical image Segmentation, Overcomplete Convolutional
Network Kite-Net (Kite-Net), Overcomplete Convolutional Network Kite-Net
(Kite-Net), Overcomplete Convolutional Network Kite-Net (Kite-Net),
Overcomplete Convolutional Network Kite-Net (Kite-Net (HarDNet-MSEG). In this
context, we will create a comprehensive computer-assisted diagnostic method
that will be verified in a randomised controlled trial. For precancers,
cervical cancer, and thyroid cancer, the model will be a highly automated
diagnostic and staging tool. This would save time and effort for professionals
when analysing medical pictures, help build a better therapeutic approach, and
give a "second opinion" on computer-assisted diagnosis.
Author(s)
Details:
Andreea Roxana Luca,
Faculty of General Medicine, “Grigore T. Popa”
University of Medicine and Pharmacy, Iasi, Romania and Departament Obstetrics
and Gynecology, Integrated Ambulatory of Hospital Sf. Spiridon”, Iasi, Romania.
Tudor Florin Ursuleanu,
Faculty of General Medicine, “Grigore T. Popa” University of Medicine and
Pharmacy, Iasi, Romania and Department of Surgery VI, “Sf. Spiridon” Hospital,
Iasi, Romania and Departament of Surgery I, Regional Institute of Oncology,
Iasi, Romania.
Liliana Gheorghe,
Faculty of General Medicine, “Grigore T. Popa” University of Medicine and
Pharmacy, Iasi, Romania and Department of Radiology, “Sf. Spiridon” Hospital,
Iasi, Romania.
Roxana Grigorovici,
Faculty of General Medicine, “Grigore T. Popa” University of Medicine and
Pharmacy, Iasi, Romania.
Stefan Iancu,
Faculty of General Medicine, “Grigore T. Popa” University of Medicine and
Pharmacy, Iasi, Romania.
Maria Hlusneac,
Faculty of General Medicine, “Grigore T. Popa” University of Medicine and
Pharmacy, Iasi, Romania.
Cristina Preda,
Faculty of General Medicine, “Grigore T. Popa” University of Medicine and
Pharmacy, Iasi, Romania and Department of Endocrinology, “Sf. Spiridon”
Hospital, Iasi, Romania.
Alexandru Grigorovici,
Faculty of General Medicine, “Grigore T. Popa” University of Medicine and
Pharmacy, Iasi, Romania and Department of Surgery VI, “Sf. Spiridon” Hospital,
Iasi, Romania.
Please see the link here: https://stm.bookpi.org/RDST-V2/article/view/6611
