Method and system of detecting and classifying an object in real-time medical imaging

A system for correcting a set of input images, comprising:

• a processor;
• and
• a memory communicably coupled to the processor, wherein the memory stores processor-executable instructions, which, on execution by the processor, cause the processor to:
• receive real-time imaging data captured by an imaging device,
• wherein the imaging data comprises a set of image frames;
• for each of the set of image frames:
• generate a pre-processed image frame based on:
• correction of one or more pixels corresponding to one or more reflections in a corresponding image frame using an autoencoder based deep learning (DL) model;
• splitting the corrected image frame into a R channel image, a G channel image, and a B channel image;
• generation of a texture enhanced G channel image by performing texture enhancement of the G channel image;
• generation of a denoised B channel image by denoising the B channel image using a Wiener filter;
• and
• generation of a color enhanced image frame from the R channel image, the texture enhanced G channel image and the denoised B channel image;
• determine at least one region of interest corresponding to at least one object in the pre-processed image frame using a Single Shot Detection (SSD) model,
• wherein the SSD model is pre-trained to detect the at least one object by extracting one or more features from the pre-processed image frame corresponding to the at least one object;
• and
• classify the at least one object as one of:
• a cancerous type, a pre-cancerous type or a non-cancerous type using a Convolution Neural Network (CNN) model.

A non-transitory computer-readable medium storing computer-executable instructions for extracting relevant data from a document image, the computer-executable instructions configured for:

• receiving real-time imaging data captured by an imaging device,
• wherein the imaging data comprises a set of image frames;
• for each of the set of image frames:
• generating a pre-processed image frame by:
• correcting one or more pixels corresponding to one or more reflections in a corresponding image frame using an autoencoder based deep learning (DL) model;
• splitting the corrected image frame into a R channel image, a G channel image, and a B channel image;
• performing texture enhancement of the G channel image;
• denoising the B channel image using a Wiener filter;
• and
• generating a color enhanced image frame from the R channel image, the texture enhanced G channel image and the denoised B channel image;
• determining at least one region of interest corresponding to at least one object in the pre-processed image frame using a Single Shot Detection (SSD) model,
• wherein the SSD model is pre-trained to detect the at least one object by extracting one or more features from the pre-processed image frame corresponding to the at least one object;
• and
• classifying the at least one object as one of:
• a cancerous type, a pre-cancerous type or a non-cancerous type, using a Convolution Neural Network (CNN) model.