3,013 research outputs found
Cancer diagnosis using deep learning: A bibliographic review
In this paper, we first describe the basics of the field of cancer diagnosis, which includes steps of cancer diagnosis followed by the typical classification methods used by doctors, providing a historical idea of cancer classification techniques to the readers. These methods include Asymmetry, Border, Color and Diameter (ABCD) method, seven-point detection method, Menzies method, and pattern analysis. They are used regularly by doctors for cancer diagnosis, although they are not considered very efficient for obtaining better performance. Moreover, considering all types of audience, the basic evaluation criteria are also discussed. The criteria include the receiver operating characteristic curve (ROC curve), Area under the ROC curve (AUC), F1 score, accuracy, specificity, sensitivity, precision, dice-coefficient, average accuracy, and Jaccard index. Previously used methods are considered inefficient, asking for better and smarter methods for cancer diagnosis. Artificial intelligence and cancer diagnosis are gaining attention as a way to define better diagnostic tools. In particular, deep neural networks can be successfully used for intelligent image analysis. The basic framework of how this machine learning works on medical imaging is provided in this study, i.e., pre-processing, image segmentation and post-processing. The second part of this manuscript describes the different deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial models (GANs), deep autoencoders (DANs), restricted Boltzmann’s machine (RBM), stacked autoencoders (SAE), convolutional autoencoders (CAE), recurrent neural networks (RNNs), long short-term memory (LTSM), multi-scale convolutional neural network (M-CNN), multi-instance learning convolutional neural network (MIL-CNN). For each technique, we provide Python codes, to allow interested readers to experiment with the cited algorithms on their own diagnostic problems. The third part of this manuscript compiles the successfully applied deep learning models for different types of cancers. Considering the length of the manuscript, we restrict ourselves to the discussion of breast cancer, lung cancer, brain cancer, and skin cancer. The purpose of this bibliographic review is to provide researchers opting to work in implementing deep learning and artificial neural networks for cancer diagnosis a knowledge from scratch of the state-of-the-art achievements
Time-Efficient Hybrid Approach for Facial Expression Recognition
Facial expression recognition is an emerging research area for improving human and computer interaction. This research plays a significant role in the field of social communication, commercial enterprise, law enforcement, and other computer interactions. In this paper, we propose a time-efficient hybrid design for facial expression recognition, combining image pre-processing steps and different Convolutional Neural Network (CNN) structures providing better accuracy and greatly improved training time. We are predicting seven basic emotions of human faces: sadness, happiness, disgust, anger, fear, surprise and neutral. The model performs well regarding challenging facial expression recognition where the emotion expressed could be one of several due to their quite similar facial characteristics such as anger, disgust, and sadness. The experiment to test the model was conducted across multiple databases and different facial orientations, and to the best of our knowledge, the model provided an accuracy of about 89.58% for KDEF dataset, 100% accuracy for JAFFE dataset and 71.975% accuracy for combined (KDEF + JAFFE + SFEW) dataset across these different scenarios. Performance evaluation was done by cross-validation techniques to avoid bias towards a specific set of images from a database
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
Invariant Scattering Transform for Medical Imaging
Over the years, the Invariant Scattering Transform (IST) technique has become
popular for medical image analysis, including using wavelet transform
computation using Convolutional Neural Networks (CNN) to capture patterns'
scale and orientation in the input signal. IST aims to be invariant to
transformations that are common in medical images, such as translation,
rotation, scaling, and deformation, used to improve the performance in medical
imaging applications such as segmentation, classification, and registration,
which can be integrated into machine learning algorithms for disease detection,
diagnosis, and treatment planning. Additionally, combining IST with deep
learning approaches has the potential to leverage their strengths and enhance
medical image analysis outcomes. This study provides an overview of IST in
medical imaging by considering the types of IST, their application,
limitations, and potential scopes for future researchers and practitioners
A critical analysis of self-supervision, or what we can learn from a single image
We look critically at popular self-supervision techniques for learning deep
convolutional neural networks without manual labels. We show that three
different and representative methods, BiGAN, RotNet and DeepCluster, can learn
the first few layers of a convolutional network from a single image as well as
using millions of images and manual labels, provided that strong data
augmentation is used. However, for deeper layers the gap with manual
supervision cannot be closed even if millions of unlabelled images are used for
training. We conclude that: (1) the weights of the early layers of deep
networks contain limited information about the statistics of natural images,
that (2) such low-level statistics can be learned through self-supervision just
as well as through strong supervision, and that (3) the low-level statistics
can be captured via synthetic transformations instead of using a large image
dataset.Comment: Accepted paper at the International Conference on Learning
Representations (ICLR) 202
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