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Deep Learning for Cancer Detection
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Deep Learning for Cancer Detection

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Wearables".

Deadline for manuscript submissions: closed (1 April 2021) | Viewed by 8439

Special Issue Editor

Prof. Dr. Francesco Mercaldo

E-Mail Website
Guest Editor
Department of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, 86100 Campobasso, Italy
Interests: computer vision; image analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Cancer is the leading cause of deaths worldwide. Both researchers and doctors are facing the challenges of fighting cancer. Early detection of cancer is the top priority for saving the lives of many. Typically, visual examination and manual techniques are used for these types of cancer diagnoses.
The rapid advancement of machine learning and especially deep learning continues to fuel the medical imaging community’s interest in applying these techniques to improve the accuracy of cancer screening.

In this Special Issue, we aim to explore the applications of deep learning for cancer detection and diagnosis. We solicit original research papers as well as review articles.

Dr. Francesco Mercaldo
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • convolutional neural networks
  • artificial neural networks
  • deep learning
  • deep learning (AI) algorithm
  • deep learning (AI) architecture
  • deep learning applications
  • Brain–computer interface
  • deep learning applications
  • intelligent bioinformatics
  • neuromorphics

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Published Papers (2 papers)

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Research

20 pages, 5527 KiB  
Article
AF-SENet: Classification of Cancer in Cervical Tissue Pathological Images Based on Fusing Deep Convolution Features
by Pan Huang, Xiaoheng Tan, Chen Chen, Xiaoyi Lv and Yongming Li
Sensors 2021, 21(1), 122; https://doi.org/10.3390/s21010122 - 27 Dec 2020
Cited by 33 | Viewed by 3641
Abstract
Cervical cancer is the fourth most common cancer in the world. Whole-slide images (WSIs) are an important standard for the diagnosis of cervical cancer. Missed diagnoses and misdiagnoses often occur due to the high similarity in pathological cervical images, the large number of [...] Read more.
Cervical cancer is the fourth most common cancer in the world. Whole-slide images (WSIs) are an important standard for the diagnosis of cervical cancer. Missed diagnoses and misdiagnoses often occur due to the high similarity in pathological cervical images, the large number of readings, the long reading time, and the insufficient experience levels of pathologists. Existing models have insufficient feature extraction and representation capabilities, and they suffer from insufficient pathological classification. Therefore, this work first designs an image processing algorithm for data augmentation. Second, the deep convolutional features are extracted by fine-tuning pre-trained deep network models, including ResNet50 v2, DenseNet121, Inception v3, VGGNet19, and Inception-ResNet, and then local binary patterns and a histogram of the oriented gradient to extract traditional image features are used. Third, the features extracted by the fine-tuned models are serially fused according to the feature representation ability parameters and the accuracy of multiple experiments proposed in this paper, and spectral embedding is used for dimension reduction. Finally, the fused features are inputted into the Analysis of Variance-F value-Spectral Embedding Net (AF-SENet) for classification. There are four different pathological images of the dataset: normal, low-grade squamous intraepithelial lesion (LSIL), high-grade squamous intraepithelial lesion (HSIL), and cancer. The dataset is divided into a training set (90%) and a test set (10%). The serial fusion effect of the deep features extracted by Resnet50v2 and DenseNet121 (C5) is the best, with average classification accuracy reaching 95.33%, which is 1.07% higher than ResNet50 v2 and 1.05% higher than DenseNet121. The recognition ability is significantly improved, especially in LSIL, reaching 90.89%, which is 2.88% higher than ResNet50 v2 and 2.1% higher than DenseNet121. Thus, this method significantly improves the accuracy and generalization ability of pathological cervical WSI recognition by fusing deep features. Full article
(This article belongs to the Special Issue Deep Learning for Cancer Detection)
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21 pages, 2470 KiB  
Article
Radiomics for Gleason Score Detection through Deep Learning
by Luca Brunese, Francesco Mercaldo, Alfonso Reginelli and Antonella Santone
Sensors 2020, 20(18), 5411; https://doi.org/10.3390/s20185411 - 21 Sep 2020
Cited by 33 | Viewed by 3944
Abstract
Prostate cancer is classified into different stages, each stage is related to a different Gleason score. The labeling of a diagnosed prostate cancer is a task usually performed by radiologists. In this paper we propose a deep architecture, based on several convolutional layers, [...] Read more.
Prostate cancer is classified into different stages, each stage is related to a different Gleason score. The labeling of a diagnosed prostate cancer is a task usually performed by radiologists. In this paper we propose a deep architecture, based on several convolutional layers, aimed to automatically assign the Gleason score to Magnetic Resonance Imaging (MRI) under analysis. We exploit a set of 71 radiomic features belonging to five categories: First Order, Shape, Gray Level Co-occurrence Matrix, Gray Level Run Length Matrix and Gray Level Size Zone Matrix. The radiomic features are gathered directly from segmented MRIs using two free-available dataset for research purpose obtained from different institutions. The results, obtained in terms of accuracy, are promising: they are ranging between 0.96 and 0.98 for Gleason score prediction. Full article
(This article belongs to the Special Issue Deep Learning for Cancer Detection)
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