Predictive Oncology Deep Learning-Based Multi-Cancer Detection

Abstract

Predictive Oncology using deep learning
enables automated multi-cancer detection
by extracting complex patterns from
high-dimensional clinical and imaging
data. Traditional cancer diagnostics are
limited by manual feature extraction and
variability in interpretation. Deep
learning techniques such as
convolutional neural networks (CNNs)
and transformer models have shown
remarkable performance in single-cancer
detection tasks. However, existing
approaches are generally constrained to a
specific cancer type, such as skin cancer,
creating a need for scalable multi-cancer
frameworks. This research proposes an
integrated deep learning model for
simultaneous detection of liver, skin, and
breast cancers. The model incorporates
multi-modal data fusion to enhance
generalization across varying cancer
presentations. Extensive