Machine Learning for disease diagnosis and prognosis using Medical Imaging

The machine learning algorithm system compares a collection of input pictures to discover the image characteristics that, when utilized, result in the accurate categorization of the image—that is, whether it depicts a benign or malignant tumor.

Deep learning has shown ground-breaking performance in various fields, including image identification, natural language processing, and voice recognition. However, the effectiveness of deep learning in predicting illness status using genetic information has received little attention.

It is utilized in various applications, including ultrasonography, magnetic resonance imaging (MRI), computed tomography (CT), PET positron emission tomography, and SPECT single-photon emission tomography. Diagnostic modalities not included in the preceding list are classified as “other subjects” (e.g., optical coherence tomography, dual-energy x-ray absorptiometry, etc.)