# Visible-Light Hyperspectral Reconstruction and PCA-Based Feature Extraction for Malignant Pleural Effusion Cytology

## TL;DR

This paper introduces a new method using hyperspectral imaging and PCA to analyze pleural effusion cytology images for faster lung cancer diagnosis.

## Contribution

A novel CAD system integrating hyperspectral imaging and PCA for cell classification in pleural effusion cytology is proposed.

## Key findings

- Hyperspectral imaging captures detailed spectral variations in stained pleural effusion samples.
- PCA effectively reduces data dimensions while preserving key features for cell classification.
- The system aims to improve diagnostic speed and accuracy for malignant pleural effusion.

## Abstract

Malignant pleural effusion, commonly referred to as MPE, is a prevalent complication associated with individuals diagnosed with neoplastic disorders. The data acquired by pleural fluid cytology is beneficial for diagnostic objectives. Consequently, the initial step in the diagnostic procedure for lung cancer is the analysis of pleural effusion fluid. This research aims to provide a cutting-edge model for analyzing PE cytology images. This model utilizes a computer-aided diagnosis (CAD) system that integrates hyperspectral imaging (HSI) technology for the classification of spectral variations. Giemsa, which is one of the most popular microscopic stains, is employed to stain the samples, after which a sensitive CCD mounted on a microscope captures the images. Subsequently, the HSI model is tasked with obtaining the image spectra. Principal Component Analysis (PCA) constitutes the concluding phase in the classification procedure of various cell types. We expect that the suggested technique will enable medical professionals to stage lung cancer more rapidly. In the future, we aspire to develop an extensive data system that utilizes deep learning techniques to facilitate the automatic classification of cells, thereby ensuring the most precise diagnosis. Furthermore, enhancing accuracy and minimizing data dimensions are important priorities to accelerate diagnostics, conserve resources, and reduce computing time.