# A Multitask CNN for Near-Infrared Probe: Enhanced Real-Time Breast Cancer Imaging

## TL;DR

This paper introduces a multitask CNN model that improves real-time breast cancer imaging using near-infrared technology, offering better accuracy and efficiency.

## Contribution

The novel multitask CNN model combines image reconstruction and classification for enhanced breast cancer detection.

## Key findings

- The model achieved an RMSE of 0.0624 and an R2 of 0.9704 in image reconstruction.
- Classification accuracies reached 100% for depth, 92.86% for length, and a perfect F1 score for health status.

## Abstract

The early detection of breast cancer, particularly in dense breast tissues, faces significant challenges with traditional imaging techniques such as mammography. This study utilizes a Near-infrared Scan (NIRscan) probe and an advanced convolutional neural network (CNN) model to enhance tumor localization accuracy and efficiency. CNN processed data from 133 breast phantoms into 266 samples using data augmentation techniques, such as mirroring. The model significantly improved image reconstruction, achieving an RMSE of 0.0624, MAE of 0.0360, R2 of 0.9704, and Fuzzy Jaccard Index of 0.9121. Subsequently, we introduced a multitask CNN that reconstructs images and classifies them based on depth, length, and health status, further enhancing its diagnostic capabilities. This multitasking approach leverages the robust feature extraction capabilities of CNNs to perform complex tasks simultaneously, thereby improving the model’s efficiency and accuracy. It achieved exemplary classification accuracies in depth (100%), length (92.86%), and health status, with a perfect F1 Score. These results highlight the promise of NIRscan technology, in combination with a multitask CNN model, as a supportive tool for improving real-time breast cancer screening and diagnostic workflows.