Medical Imaging Using Deep Learning

Abstract

The healthcare sector has been transformed by deep learning, a kind of artificial intelligence Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are two examples of deep learning techniques that have been used to evaluate medical pictures, forecast illness outcomes, and enhance patient care. This study examines the important strides made by deep learning in the fields of radiology, pathology, genomics, and electronic health records (EHRs). Additionally, it draws attention to the difficulties and moral issues that come with the application of deep learning in healthcare, highlighting the necessity of strong data protection and model interpretability. Deep learning's potential and promising results in healthcare highlight its revolutionary effects on patient care, diagnosis, and treatment, ultimately raising the standard of care.

Ensemble and Transformer Encoder-based Models for the Cervical Cancer Classification Using Pap-smear Images

“…To enhance the clarity and quality of images a preprocessing step was carried out on the medical data. This involved adjusting intensities resizing images and eliminating unnecessary information to prepare them for machine learning algorithms [41]. The dataset was divided randomly into training, testing and validation sets.…”

“…A. Preprocessing Medical data preprocessing is essential for adequately preparing trainable medical images. It involves removing unwanted or irrelevant information for AI classifiers, enhancing the spatial resolution and quality of the images, and normalizing and resizing the pixel intensities to ensure they fall within a consistent grayscale range for all images [41] . Moreover, skilled radiologists carefully establish the precise boundaries of the cancerous cell in each pap image, exclusively focusing on these specific areas rather than utilizing the entire image, for the purpose of training AI models.…”