A Deep Convolutional Neural Network for Lung Cancer Diagnostic

TL;DR

This paper presents a novel deep convolutional neural network architecture designed for accurate lung cancer diagnosis from medical images, demonstrating promising results on a large Kaggle dataset.

Contribution

The paper introduces a new CNN architecture that learns high-level, discriminant features for lung cancer classification with low variance, improving upon existing models.

Findings

Achieved high classification accuracy on KDSB17 dataset

Compared favorably with other models in Kaggle competition

Demonstrated robustness in medical image analysis

Abstract

In this paper, we examine the strength of deep learning technique for diagnosing lung cancer on medical image analysis problem. Convolutional neural networks (CNNs) models become popular among the pattern recognition and computer vision research area because of their promising outcome on generating high-level image representations. We propose a new deep learning architecture for learning high-level image representation to achieve high classification accuracy with low variance in medical image binary classification tasks. We aim to learn discriminant compact features at beginning of our deep convolutional neural network. We evaluate our model on Kaggle Data Science Bowl 2017 (KDSB17) data set, and compare it with some related works proposed in the Kaggle competition.