Classification techniques in breast cancer diagnosis: A systematic literature review

Tarin-Loaded Nanoliposomes Activate Apoptosis and Autophagy and Inhibit the Migration of Human Mammary Adenocarcinoma Cells

Background

Tarin, a lectin purified from Colocasia esculenta, promotes in vitro and in vivo immunomodulatory effects allied to promising anticancer and antimetastatic effects against human adenocarcinoma mammary cells. This makes this 47 kDa-protein a natural candidate against human breast cancer, a leading cause of death among women. Tarin encapsulated in pegylated nanoliposomes displays increased effectiveness in controlling the proliferation of a mammary adenocarcinoma lineage comprising MDA-MB-231 cells.

Methods

The mechanisms enrolled in anticancer and antimetastatic responses were investigated by treating MDA-MB-231 cells with nano-encapsulated tarin at 72 μg/mL for up to 48h through flow cytometry and transmission electron microscopy (TEM). The safety of nano-encapsulated tarin towards healthy tissue was also assessed by the resazurin viability assay, and the effect of nanoencapsulated tarin on cell migration was evaluated by scratch assays.

Results

Ultrastructural analyses of MDA-MB-231 cells exposed to nanoencapsulated tarin revealed the accumulation of autophagosomes and damaged organelles, compatible with autophagy-dependent cell death. On the other hand, the flow cytometry investigation detected the increased occurrence of acidic vacuolar organelles, a late autophagosome trait, along with the enhanced presence of apoptotic cells, activated caspase-3/7, and cell cycle arrest at G0/G1. No deleterious effects were observed in healthy fibroblast cells following tarin nanoencapsulated exposition, in contrast to reduced viability in cells exposed to free tarin. The migration of MDA-MB-231 cells was inhibited by nano-encapsulated tarin, with delayed movement by 24 h compared to free tarin.

Conclusion

The nanoliposome formulation delivers tarin in a delayed and sustained manner, as evidenced by the belated and potent antitumoral and anti-migration effects on adenocarcinoma cells, with no toxicity to healthy cells. Although further investigations are required to fully understand antitumorigenic tarin mechanisms, the activation of both apoptotic and autophagic machineries along with the caspase-3/7 pathway, and cell cycle arrest may comprise a part of these mechanisms.

An efficient transfer learning based cross model classification (TLBCM) technique for the prediction of breast cancer

Breast cancer has been the most life-threatening disease in women in the last few decades. The high mortality rate among women is due to breast cancer because of less awareness and a minimum number of medical facilities to detect the disease in the early stages. In the recent era, the situation has changed with the help of many technological advancements and medical equipment to observe breast cancer development. The machine learning technique supports vector machines (SVM), logistic regression, and random forests have been used to analyze the images of cancer cells on different data sets. Although the particular technique has performed better on the smaller data set, accuracy still needs to catch up in most of the data, which needs to be fairer to apply in the real-time medical environment. In the proposed research, state-of-the-art deep learning techniques, such as transfer learning, based cross model classification (TLBCM), convolution neural network (CNN) and transfer learning, residual network (ResNet), and Densenet proposed for efficient prediction of breast cancer with the minimized error rating. The convolution neural network and transfer learning are the most prominent techniques for predicting the main features in the data set. The sensitive data is protected using a cyber-physical system (CPS) while using the images virtually over the network. CPS act as a virtual connection between human and networks. While the data is transferred in the network, it must monitor using CPS. The ResNet changes the data on many layers without compromising the minimum error rate. The DenseNet conciliates the problem of vanishing gradient issues. The experiment is carried out on the data sets Breast Cancer Wisconsin (Diagnostic) and Breast Cancer Histopathological Dataset (BreakHis). The convolution neural network and the transfer learning have achieved a validation accuracy of 98.3%. The results of these proposed methods show the highest classification rate between the benign and the malignant data. The proposed method improves the efficiency and speed of classification, which is more convenient for discovering breast cancer in earlier stages than the previously proposed methodologies.

Intelligent Analysis of Exercise Health Big Data Based on Deep Convolutional Neural Network

In this paper, the algorithm of the deep convolutional neural network is used to conduct in-depth research and analysis of sports health big data, and an intelligent analysis system is designed for the practical process. A convolutional neural network is one of the most popular methods of deep learning today. The convolutional neural network has the feature of local perception, which allows a complete image to be divided into several small parts, by learning the characteristic features of each local part and then merging the local information at the high level to get the full representation information. In this paper, we first apply a convolutional neural network for four classifications of brainwave data and analyze the accuracy and recall of the model. The model is then further optimized to improve its accuracy and is compared with other models to confirm its effectiveness. A demonstration platform of emotional fatigue detection with multimodal data feature fusion was established to realize data acquisition, emotional fatigue detection, and emotion feedback functions. The emotional fatigue detection platform was tested to verify that the proposed model can be used for time-series data feature learning. According to the platform requirement analysis and detailed functional design, the development of each functional module of the platform was completed and system testing was conducted. The big data platform constructed in this study can meet the basic needs of health monitoring for data analysis, which is conducive to the formation of a good situation of orderly and effective interaction among multiple subjects, thus improving the information service level of health monitoring and promoting comprehensive health development.

Reviewing Machine Learning and Image Processing Based Decision-Making Systems for Breast Cancer Imaging

Breast cancer (BC) is the leading cause of death among women worldwide. It affects in general women older than 40 years old. Medical images analysis is one of the most promising research areas since it provides facilities for diagnosis and decision-making of several diseases such as BC. This paper conducts a Structured Literature Review (SLR) of the use of Machine Learning (ML) and Image Processing (IP) techniques to deal with BC imaging. A set of 530 papers published between 2000 and August 2019 were selected and analyzed according to ten criteria: year and publication channel, empirical type, research type, medical task, machine learning techniques, datasets used, validation methods, performance measures and image processing techniques which include image pre-processing, segmentation, feature extraction and feature selection. Results showed that diagnosis was the most used medical task and that Deep Learning techniques (DL) were largely used to perform classification. Furthermore, we found out that classification was the most ML objective investigated followed by prediction and clustering. Most of the selected studies used Mammograms as imaging modalities rather than Ultrasound or Magnetic Resonance Imaging with the use of public or private datasets with MIAS as the most frequently investigated public dataset. As for image processing techniques, the majority of the selected studies pre-process their input images by reducing the noise and normalizing the colors, and some of them use segmentation to extract the region of interest with the thresholding method. For feature extraction, we note that researchers extracted the relevant features using classical feature extraction techniques (e.g. Texture features, Shape features, etc.) or DL techniques (e. g. VGG16, VGG19, ResNet, etc.), and finally few papers used feature selection techniques in particular the filter methods.