# Computer-Aided Cancer Diagnosis via Machine Learning and Deep Learning: A comparative review

arXiv: https://arxiv.org/abs/2210.11943 · HTML: https://mlsanity.com/paper/2210.11943/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 252 references that are in the map.
- First posted on arXiv 19 Oct 2022.

## Builds on (most cited first, 17 of 17)

- [Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images](https://mlsanity.com/paper/2004.09666/) (2020)
- [Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening](https://mlsanity.com/paper/1903.08297/) (2019)
- [An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization](https://mlsanity.com/paper/2002.07613/) (2020)
- [Robust breast cancer detection in mammography and digital breast tomosynthesis using annotation-efficient deep learning approach](https://mlsanity.com/paper/1912.11027/) (2019)
- [A 3D Probabilistic Deep Learning System for Detection and Diagnosis of Lung Cancer Using Low-Dose CT Scans](https://mlsanity.com/paper/1902.03233/) (2019)
- [Melanoma Detection using Adversarial Training and Deep Transfer Learning](https://mlsanity.com/paper/2004.06824/) (2020)
- [Learning to detect chest radiographs containing lung nodules using visual attention networks](https://mlsanity.com/paper/1712.00996/) (2017)
- [Breast Cancer Classification from Histopathological Images with Inception Recurrent Residual Convolutional Neural Network](https://mlsanity.com/paper/1811.04241/) (2018)
- [Overcoming the limitations of patch-based learning to detect cancer in whole slide images](https://mlsanity.com/paper/2012.00617/) (2020)
- [The Effects of Skin Lesion Segmentation on the Performance of Dermatoscopic Image Classification](https://mlsanity.com/paper/2008.12602/) (2020)
- [Evolving the pulmonary nodules diagnosis from classical approaches to deep learning aided decision support: three decades development course and future prospect](https://mlsanity.com/paper/1901.07858/) (2019)
- [Dual Convolutional Neural Networks for Breast Mass Segmentation and Diagnosis in Mammography](https://mlsanity.com/paper/2008.02957/) (2020)
- [Integration of Convolutional Neural Networks for Pulmonary Nodule Malignancy Assessment in a Lung Cancer Classification Pipeline](https://mlsanity.com/paper/1912.08679/) (2019)
- [Boosted EfficientNet: Detection of Lymph Node Metastases in Breast Cancer Using Convolutional Neural Network](https://mlsanity.com/paper/2010.05027/) (2020)
- [Predicting Lung Nodule Malignancies by Combining Deep Convolutional Neural Network and Handcrafted Features](https://mlsanity.com/paper/1809.02333/) (2018)
- [A Two-Stage Multiple Instance Learning Framework for the Detection of Breast Cancer in Mammograms](https://mlsanity.com/paper/2004.11726/) (2020)
- [Bucket of deep transfer learning features and classification models for melanoma detection](https://mlsanity.com/paper/2009.08639/) (2020)

## Abstract (arXiv)

The past years have seen a considerable increase in cancer cases. However, a cancer diagnosis is often complex and depends on the types of images provided for analysis. It requires highly skilled practitioners but is often time-consuming and error-prone. If Machine Learning and deep learning algorithms have been widely used, a comprehensive review of the techniques used from the pre-processing steps to the final prediction is lacking. With this review, we aim to provide a comprehensive overview of the current steps required in building efficient and accurate machine learning algorithm for cancer prediction, detection and classification. To do so, we compile the results of cancer related study using AI over the past years. We include various cancers that encompass different types of images, and therefore different related techniques. We show that tremendous improvements have been made in the early detection of cancerous tumors and tissues. The techniques used are various and often problem-tailored and our findings is confirmed through the study of a large number of research. Moreover, we investigate the approaches best suited for different types of images such as histology, dermoscopic, MRI, etc. With this work, we summarize the main finding over the past years in cancer detection using deep learning techniques. We discuss the challenges of cancer research related to the large discrepancies in the images, and we provide some notable results in the field for lung, breast, and skin cancers.