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

Using Multi-level Convolutional Neural Network for Classification of Lung Nodules on CT images

© 2018 IEEE. Lung cancer is one of the four major cancers in the world. Accurate diagnosing of lung cancer in the early stage plays an important role to increase the survival rate. Computed Tomography (CT)is an effective method to help the doctor to detect the lung cancer. In this paper, we developed a multi-level convolutional neural network (ML-CNN)to investigate the problem of lung nodule malignancy classification. ML-CNN consists of three CNNs for extracting multi-scale features in lung nodule CT images. Furthermore, we flatten the output of the last pooling layer into a one-dimensional vector for every level and then concatenate them. This strategy can help to improve the performance of our model. The ML-CNN is applied to ternary classification of lung nodules (benign, indeterminate and malignant lung nodules). The experimental results show that our ML-CNN achieves 84.81\% accuracy without any additional hand-craft preprocessing algorithm. It is also indicated that our model achieves the best result in ternary classification

Deep learning for identifying Lung Diseases

Growing health problems, such as lung diseases, especially for children and the elderly, require better diagnostic methods, such as computer-based solutions, and it is crucial to detect and treat these problems early. The purpose of this article is to design and implement a new computer vision-based algorithm based on lung disease diagnosis, which has better performance in lung disease recognition than previous models to reduce lung-related health problems and costs . In addition, we have improved the accuracy of the five lung diseases detection, which helps doctors and doctors use computers to solve this problem at an early stage

LCDctCNN: Lung Cancer Diagnosis of CT scan Images Using CNN Based Model

The most deadly and life-threatening disease in the world is lung cancer. Though early diagnosis and accurate treatment are necessary for lowering the lung cancer mortality rate. A computerized tomography (CT) scan-based image is one of the most effective imaging techniques for lung cancer detection using deep learning models. In this article, we proposed a deep learning model-based Convolutional Neural Network (CNN) framework for the early detection of lung cancer using CT scan images. We also have analyzed other models for instance Inception V3, Xception, and ResNet-50 models to compare with our proposed model. We compared our models with each other considering the metrics of accuracy, Area Under Curve (AUC), recall, and loss. After evaluating the model's performance, we observed that CNN outperformed other models and has been shown to be promising compared to traditional methods. It achieved an accuracy of 92%, AUC of 98.21%, recall of 91.72%, and loss of 0.328.Comment: 8, accepted by 10th International Conference on Signal Processing and Integrated Networks (SPIN 2023

Lung nodules identification in CT scans using multiple instance learning.

Computer Aided Diagnosis (CAD) systems for lung nodules diagnosis aim to classify nodules into benign or malignant based on images obtained from diverse imaging modalities such as Computer Tomography (CT). Automated CAD systems are important in medical domain applications as they assist radiologists in the time-consuming and labor-intensive diagnosis process. However, most available methods require a large collection of nodules that are segmented and annotated by radiologists. This process is labor-intensive and hard to scale to very large datasets. More recently, some CAD systems that are based on deep learning have emerged. These algorithms do not require the nodules to be segmented, and radiologists need to only provide the center of mass of each nodule. The training image patches are then extracted from volumes of fixed-sized centered at the provided nodule\u27s center. However, since the size of nodules can vary significantly, one fixed size volume may not represent all nodules effectively. This thesis proposes a Multiple Instance Learning (MIL) approach to address the above limitations. In MIL, each nodule is represented by a nested sequence of volumes centered at the identified center of the nodule. We extract one feature vector from each volume. The set of features for each nodule are combined and represented by a bag. Next, we investigate and adapt some existing algorithms and develop new ones for this application. We start by applying benchmark MIL algorithms to traditional Gray Level Co-occurrence Matrix (GLCM) engineered features. Then, we design and train simple Convolutional Neural Networks (CNNs) to learn and extract features that characterize lung nodules. These extracted features are then fed to a benchmark MIL algorithm to learn a classification model. Finally, we develop new algorithms (MIL-CNN) that combine feature learning and multiple instance classification in a single network. These algorithms generalize the CNN architecture to multiple instance data. We design and report the results of three experiments applied on both generative (GLCM) and learned (CNN) features using two datasets (The Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) \cite{armato2011lung} and the National Lung Screening Trial (NLST) \cite{national2011reduced}). Two of these experiments perform five-fold cross-validations on the same dataset (NLST or LIDC). The third experiment trains the algorithms on one collection (NLST dataset) and tests it on the other (LIDC dataset). We designed our experiments to compare the different features, compare MIL versus Single Instance Learning (SIL) where a single feature vector represents a nodule, and compare our proposed end-to-end MIL approaches to existing benchmark MIL methods. We demonstrate that our proposed MIL-CNN frameworks are more accurate for the lung nodules diagnosis task. We also show that MIL representation achieves better results than SIL applied on the ground truth region of each nodule