# End-to-End Discriminative Deep Network for Liver Lesion Classification

## TL;DR

This paper presents an end-to-end deep learning model that accurately classifies liver lesions in CT images, aiding early diagnosis and treatment of liver metastases versus benign cysts.

## Contribution

It introduces a novel deep network architecture combining InceptionV3 with residual connections and pre-trained ImageNet weights for liver lesion classification.

## Key findings

- Achieved 96% accuracy in lesion classification.
- Surpassed existing state-of-the-art methods.
- Demonstrated potential for clinical radiology integration.

## Abstract

Colorectal liver metastasis is one of most aggressive liver malignancies. While the definition of lesion type based on CT images determines the diagnosis and therapeutic strategy, the discrimination between cancerous and non-cancerous lesions are critical and requires highly skilled expertise, experience and time. In the present work we introduce an end-to-end deep learning approach to assist in the discrimination between liver metastases from colorectal cancer and benign cysts in abdominal CT images of the liver. Our approach incorporates the efficient feature extraction of InceptionV3 combined with residual connections and pre-trained weights from ImageNet. The architecture also includes fully connected classification layers to generate a probabilistic output of lesion type. We use an in-house clinical biobank with 230 liver lesions originating from 63 patients. With an accuracy of 0.96 and a F1-score of 0.92, the results obtained with the proposed approach surpasses state of the art methods. Our work provides the basis for incorporating machine learning tools in specialized radiology software to assist physicians in the early detection and treatment of liver lesions.