Query: cancer diseases  based on deep learning ieee

title: High Precision Phase Recovery for Single Frame Fringe Pattern of Label free Cells Detection Based on Deep Learning
abstract: As the basic unit of organism composition and life activity, the change of physiological state of cell is important to clinical disease prediction and diagnosis, especially blood diseases. In order to obtain the morphology of blood cells with abundant information content in 3D space without any biochemical or other complex processing for samples, this study proposed a transverse shear interference 3D imaging detection method for real time dynamic label free living cells based on deep learning. The phase extraction and recovery method of single red blood cell interference fringe image obtained by quantitative phase imaging system is carried out by Generating Antagonism Network (GAN) This method has a great improvement in efficiency and accuracy, it has a profound impact on the study of biological cells, and can be extended to the fields of cancer diagnosis and drug development in genomics.

title: An Automatic Cell Nuclei Segmentation based on Deep Learning Strategies
abstract: Automatic analysis of histopathology specimens images can be utilized in early extraction and detection of diseases such brain tumor, breast malignancy, colon cancer etc. The early detection of cancer may allow patients to take proper treatment. In this paper, an automatic cell nuclei segmentation based on deep learning strategies using 2 $D$ histological images is proposed. In the proposed approach U Net architecture is used and its hyper parameters are tuned to segment the cell nuclei. The proposed solution is built upon the highly adaptive nature of U Net architecture. The task of nuclei segmentation in the proposed approach includes detection of nuclei in an image and extracting the foreground, while segmenting the connected foreground area into separated nuclei masks. In the experimental results the proposed approach is tested using the dataset having histopathological cell images of breast cancer. The results shows that the proposed deep learning based approach achieved the 86 average accuracy in segmentation of cell nuclei and also outperforms the other deep learning architectures.

title: Wireless Capsule Endoscopy: A New Tool for Cancer Screening in the Colon With Deep Learning Based Polyp Recognition
abstract: Accurate recognition of polyps is crucial for early colorectal cancer diagnosis and treatment. Wireless capsule endoscopy (WCE) is a noninvasive, wireless imaging tool that allows direct visualization of the entire colon without discomfort to patients and has the potential to revolutionize the screening workup for colorectal diseases. However, current manual review is laborious and time consuming, requiring the undivided concentration of the gastroenterologist. Computational methods that can assist automated polyp recognition will enhance the outcome both in terms of diagnostic accuracy and efficiency of WCE. This review introduces the computer assisted algorithms as applied to colorectal polyp screening, focusing on the successes of deep learning based strategies in the WCE sequences. We survey key applications of WCE polyp recognition, covering deep learning based image level classification, lesion region detection, and pixel accurate segmentation. We conclude by discussing emerging research challenges, possible trends, and future directions.

title: Optimal Feature Selection Based Medical Image Classification Using Deep Learning Model in Internet of Medical Things
abstract: Internet of Medical Things (IoMT) is the collection of medical devices and related applications which link the healthcare IT systems through online computer networks. In the field of diagnosis, medical image classification plays an important role in prediction and early diagnosis of critical diseases. Medical images form an indispensable part of a patient's health record which can be applied to control, handle and treat the diseases. But, classification of images is a challenging task in computer based diagnostics. In this research article, we have introduced a improved classifier i.e. Optimal Deep Learning (DL) for classification of lung cancer, brain image, and Alzheimer's disease. The researchers proposed the Optimal Feature Selection based Medical Image Classification using DL model by incorporating preprocessing, feature selection and classification. The main goal of the paper is to derive an optimal feature selection model for effective medical image classification. To enhance the performance of the DL classifier, Opposition based Crow Search (OCS) algorithm is proposed. The OCS algorithm picks the optimal features from pre processed images, here Multi texture, grey level features were selected for the analysis. Finally, the optimal features improved the classification result and increased the accuracy, specificity and sensitivity in the diagnosis of medical images. The proposed results were implemented in MATLAB and compared with existing feature selection models and other classification approaches. The proposed model achieved the maximum performance in terms of accuracy, sensitivity and specificity being 95.22% 86.45 and 100% for the applied set of images.

title: Deep Learning Assisted Efficient AdaBoost Algorithm for Breast Cancer Detection and Early Diagnosis
abstract: Breast cancer is one of the most dangerous diseases and the second largest cause of female cancer death. Breast cancer starts when malignant, cancerous lumps start to grow from the breast cells. Self tests and Periodic clinical checks help to early diagnosis and thereby improve the survival chances significantly. The breast cancer classification is a medical method that provides researchers and scientists with a great challenge. Neural networks have recently become a popular tool in cancer data classification. In this paper, Deep Learning assisted Efficient Adaboost Algorithm (DLA EABA) for breast cancer detection has been mathematically proposed with advanced computational techniques. In addition to traditional computer vision approaches, tumor classification methods using transfers are being actively developed through the use of deep convolutional neural networks (CNNs) This study starts with examining the CNN based transfer learning to characterize breast masses for different diagnostic, predictive tasks or prognostic or in several imaging modalities, such as Magnetic Resonance Imaging (MRI) Ultrasound (US) digital breast tomosynthesis and mammography. The deep learning framework contains several convolutional layers, LSTM, Max pooling layers. The classification and error estimation that has been included in a fully connected layer and a softmax layer. This paper focuses on combining these machine learning approaches with the methods of selecting features and extracting them through evaluating their output using classification and segmentation techniques to find the most appropriate approach. The experimental results show that the high accuracy level of 97.2% Sensitivity 98.3% and Specificity 96.5% has been compared to other existing systems.

title: Deep Learning Assisted Predict of Lung Cancer on Computed Tomography Images Using the Adaptive Hierarchical Heuristic Mathematical Model
abstract: Lung cancer is known to be one of the most dangerous diseases which are the main reason for disease and death when diagnosed in primitive stages. Since lung cancer can only be detected more broadly after it spread to lung parts and the occurrence of lung cancer in the earlier stage is very difficult to predict. It causes a greater risk as radiologists and specialist doctors assess the existence of lung cancer. For this reason, it is important to build a smart and automatic cancer prediction system that is accurate and at which stage of cancer or to improve the accuracy of the previous cancer prediction that will help determines the type of treatment and treatment depth depending on the severity of the disease. In this paper, the Adaptive Hierarchical Heuristic Mathematical Model (AHHMM) has been proposed for the deep learning approach. To analyze deep learning based on the historical therapy scheme in the development of Non Small Cell Lung Cancers (NSCLC) automated radiation adaptation protocols that aim at optimizing local tumor regulation at lower rates of grade 2 RP2 radiation pneumonitis. Furthermore, the system proposed consists of several steps including acquiring the image, preprocessing, binarization, thresholding, and segmentation, extraction of features and detection of deep neural network (DNN) Segmentation of the lung CT image is carried out to extract any significant feature of a segmented image, and a specific feature extraction method is implemented. The test evaluation showed that the model proposed could detect 96.67 accuracy of the absence or presence of lung cancer.

title: Artificial Intelligence Technique for Gene Expression by Tumor RNA Seq Data: A Novel Optimized Deep Learning Approach
abstract: Cancer is one of the most feared and aggressive diseases in the world and is responsible for more than 9 million deaths universally. Staging cancer early increases the chances of recovery. One staging technique is RNA sequence analysis. Recent advances in the efficiency and accuracy of artificial intelligence techniques and optimization algorithms have facilitated the analysis of human genomics. This paper introduces a novel optimized deep learning approach based on binary particle swarm optimization with decision tree (BPSO DT) and convolutional neural network (CNN) to classify different types of cancer based on tumor RNA sequence (RNA Seq) gene expression data. The cancer types that will be investigated in this research are kidney renal clear cell carcinoma (KIRC) breast invasive carcinoma (BRCA) lung squamous cell carcinoma (LUSC) lung adenocarcinoma (LUAD) and uterine corpus endometrial carcinoma (UCEC) The proposed approach consists of three phases. The first phase is preprocessing, which at first optimize the high dimensional RNA seq to select only optimal features using BPSO DT and then, converts the optimized RNA Seq to 2D images. The second phase is augmentation, which increases the original dataset of 2086 samples to be 5 times larger. The selection of the augmentations techniques was based achieving the least impact on manipulating the features of the images. This phase helps to overcome the overfitting problem and trains the model to achieve better accuracy. The third phase is deep CNN architecture. In this phase, an architecture of two main convolutional layers for featured extraction and two fully connected layers is introduced to classify the 5 different types of cancer according to the availability of images on the dataset. The results and the performance metrics such as recall, precision and F1 score show that the proposed approach achieved an overall testing accuracy of 96.90% The comparative results are introduced, and the proposed method outperforms those in related works in terms of testing accuracy for 5 classes of cancer. Moreover, the proposed approach is less complex and consume less memory.

title: A Visually Interpretable Deep Learning Framework for Histopathological Image Based Skin Cancer Diagnosis
abstract: Owing to the high incidence rate and the severe impact of skin cancer, the precise diagnosis of malignant skin tumors is a significant goal, especially considering treatment is normally effective if the tumor is detected early. Limited published histopathological image sets and the lack of an intuitive correspondence between the features of lesion areas and a certain type of skin cancer pose a challenge to the establishment of high quality and interpretable computer aided diagnostic (CAD) systems. To solve this problem, a light weight attention mechanism based deep learning framework, namely, DRANet, is proposed to differentiate 11 types of skin diseases based on a real histopathological image set collected by us during the last 10 years. The CAD system can output not only the name of a certain disease but also a visualized diagnostic report showing possible areas related to the disease. The experimental results demonstrate that the DRANet obtains significantly better performance than baseline models (i.e. InceptionV3, ResNet50, VGG16, and VGG19) with comparable parameter size and competitive accuracy with fewer model parameters. Visualized results produced by the hidden layers of the DRANet actually highlight part of the class specific regions of diagnostic points and are valuable for decision making in the diagnosis of skin diseases.

title: Stacking and Chaining of Normalization Methods in Deep Learning Based Classification of Colorectal Cancer Using Gut Microbiome Data
abstract: Machine learning (ML) based detection of diseases using sequence based gut microbiome data has been of great interest within the artificial intelligence in medicine (AIM) community. The approach offers a non invasive alternative for colorectal cancer detection, which is based on stool samples. Considering limitations of existing methods in CRC detection, medical research has shown interest in the use of high throughput data to identify the disease. Owing to several limitations of conventional ML algorithms, deep learning (DL) methods are becoming more popular due to their outstanding performance in related fields. However, the performance of DL methods is affected by limitations such as dimensionality, sparsity, and feature dominance inherent in microbiome data. This research proposes stacking and chaining of normalization methods to address the limitations. While the stacking technique offers a robust, easy to use, and interpretable alternative for augmenting microbiome and other tabular data, the chaining technique is an alternative to data normalization that dynamically adjusts the underlying properties of data towards the normal distribution. The proposed techniques are combined with rank transformation and feature selection to further improve the performance of the model, with area under the curve (AUC) values between 0.857 to 0.987 using publicly available datasets.

title: Unsupervised Learning of Deep Learned Features from Breast Cancer Images
abstract: Detecting cancer manually in whole slide images requires significant time and effort on the laborious process. Recent advances in whole slide image analysis have stimulated the growth and development of machine learning based approaches that improve the efficiency and effectiveness in the diagnosis of cancer diseases. In this paper, we propose an unsupervised learning approach for detecting cancer in breast invasive carcinoma (BRCA) whole slide images. The proposed method is fully automated and does not require human involvement during the unsupervised learning procedure. We demonstrate the effectiveness of the proposed approach for cancer detection in BRCA and show how the machine can choose the most appropriate clusters during the unsupervised learning procedure. Moreover, we present a prototype application that enables users to select relevant groups mapping all regions related to the groups in whole slide images.