Medical Imaging Diagnostic


Medical imaging diagnostics

Software that uses machine learning algorithms, image processing, and computer vision techniques to analyze, interpret, and diagnose medical conditions from images acquired through imaging technologies such as X-rays, magnetic resonance imaging (MRI), computed tomography (CT) scans, ultrasounds, among others.

Main features

Computer vision

Processing and analysis of medical images, identifying anatomical structures, patterns, and relevant features for diagnosis.

Machine learning and deep learning:

Machine learning and deep learning, such as convolutional neural networks (CNN), to improve their ability to recognize and diagnose medical conditions accurately.

Detection and segmentation

Identification of areas of interest in medical images, such as lesions, tumors, vascular anomalies, among others, and segmentation from the surrounding environment.

Feature extraction

Extraction of relevant features from medical images, such as shape, size, texture, contrast, and intensity, which may be indicative of specific medical conditions.

Classification and diagnosis models

Construction and training of classification and diagnosis models using training data and extracted features

Integration and clinical support

Integration into medical information systems and clinical workflows, providing physicians and healthcare professionals with diagnostic support tools that can improve the accuracy, speed, and efficiency of medical image interpretation


Applicable in a variety of fields

It can have applications in various areas of medicine, such as oncology, neurology, cardiology, orthopedics, among others. It can contribute to earlier and more accurate detection of diseases and medical conditions, which can improve treatment outcomes and patients’ quality of life

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