Project Abstract

Cancer is a heterogeneous disorder comprising various types and sub-types. Early detection, screening, and diagnosis of cancer types are necessary for facilitating cancer research in early diagnosis, management, and the evolution of successful therapies. Existing methodologies were only able to classify and diagnose a single variety of cancer based on a homogeneous dataset but more focused on predicting patient survivability then cure. This research defines a machine learning-based methodology to develop a universal approach in diagnosis, detection, symptoms-based prediction, and screening of histopathology cancer, their types, and sub types using a heterogeneous dataset based on images and scans. In this architecture, we can use VGG-19 based 3D-Convolutional Neural Network for deep featureextraction and later perform regression using a random forest algorithm. We create a heterogeneous dataset consisting of results from laboratory tests, imaging tests and biopsy reports, not only relying on clinical images.Keywords: Histopathology Cancer, AI, Convolutional Neural Network, Medical Imaging, Random forest.

Keywords: Histopathology Cancer, Convolutional Neural Network, Artificial Intelligence

 Conference Details

Session: Presentation Stream 10 at Presentation Slot 3

Location: CoFo 002 at Tuesday 7th 13:30 – 17:00

Markers: Randell Gaya, George Brooks (GTA)

Course: MSc Data Science, Masters PG

Future Plans: I’m looking for an industry placement