Breast Mass Detection and Classification Using Machine Learning Approaches on Two-Dimensional Mammogram: A Review.

Breast cancer is a leading cause of mortality among women, both in India and globally. The prevalence of breast masses is notably common in women aged 20 to 60. These breast masses are classified, according to the breast imaging-reporting and data systems (BI-RADS) standard, into categories such as fibroadenoma, breast cysts, benign, and malignant masses. To aid in the diagnosis of breast disorders, imaging plays a vital role, with mammography being the most widely used modality for detecting breast abnormalities over the years. However, the process of identifying breast diseases through mammograms can be time-consuming, requiring experienced radiologists to review a significant volume of images. Early detection of breast masses is crucial for effective disease management, ultimately reducing mortality rates. To address this challenge, advancements in image processing techniques, specifically utilizing artificial intelligence (AI) and machine learning (ML), have tiled the way for the development of decision support systems. These systems assist radiologists in the accurate identification and classification of breast disorders. This paper presents a review of various studies where diverse machine learning approaches have been applied to digital mammograms. These approaches aim to identify breast masses and classify them into distinct subclasses such as normal, benign and malignant. Additionally, the paper highlights both the advantages and limitations of existing techniques, offering valuable insights for the benefit of future research endeavors in this critical area of medical imaging and breast health.

Role of 5G Networks in Healthcare Management System.

The present-day healthcare system operates on a 4G network, where the data rate needed for many IoT devices is impossible. Also, the latency involved in the network does not support the use of many devices in the network. The 5G-based cellular technology promises an effective healthcare management system with high speed and low latency. The 5G communication technology will replace the 4G technology to satisfy the increasing demand for high data rates. It incorporates higher frequency bands of around 100 MHz using millimetre waves and broadband modulation schemes. It is aimed at providing low latency while supporting real-time machine-to-machine communication. It requires a more significant number of antennas, with an average base station density three times higher than 4G. However, the rise in circuit and processing power for multiple antennas and transceivers deteriorates energy efficiency. Also, the data transmission power for 5G is three times higher than for 4G technology. One of the advanced processors used in today's mobile equipment is NVIDIA Tegra, which has a multicore system on chip (SoC) architecture with two ARM Cortex CPU cores to handle audio, images, and video. The state-of-the-art software coding using JAVA or Python has achieved smooth data transmission from mobile equipment, desktop or laptop through the internet with the support of 5G communication technology. This paper discusses some key areas related to 5G-based healthcare systems such as the architecture, antenna designs, power consumption, file protocols, security, and health implications of 5G networks.

Hybrid Transfer Learning for Classification of Uterine Cervix Images for Cervical Cancer Screening.

Transfer learning using deep pre-trained convolutional neural networks is increasingly used to solve a large number of problems in the medical field. In spite of being trained using images with entirely different domain, these networks are flexible to adapt to solve a problem in a different domain too. Transfer learning involves fine-tuning a pre-trained network with optimal values of hyperparameters such as learning rate, batch size, and number of training epochs. The process of training the network identifies the relevant features for solving a specific problem. Adapting the pre-trained network to solve a different problem requires fine-tuning until relevant features are obtained. This is facilitated through the use of large number of filters present in the convolutional layers of pre-trained network. A very few features out of these features are useful for solving the problem in a different domain, while others are irrelevant, use of which may only reduce the efficacy of the network. However, by minimizing the number of filters required to solve the problem, the efficiency of the training the network can be improved. In this study, we consider identification of relevant filters using the pre-trained networks namely AlexNet and VGG-16 net to detect cervical cancer from cervix images. This paper presents a novel hybrid transfer learning technique, in which a CNN is built and trained from scratch, with initial weights of only those filters which were identified as relevant using AlexNet and VGG-16 net. This study used 2198 cervix images with 1090 belonging to negative class and 1108 to positive class. Our experiment using hybrid transfer learning achieved an accuracy of 91.46%.

Pattern Classification of Images from Acetic Acid-Based Cervical Cancer Screening: A Review.

Automated analysis of digital cervix images acquired during visual inspection with acetic acid (VIA) is found to be of great help to physicians in diagnosing cervical cancer. Application of 3-5% acetic acid to the cervix turns abnormal lesions white, while normal lesions remain unchanged. Digital images of the cervix can be acquired during VIA procedure and can be analyzed using image-processing algorithms. Three main attributes to be considered for analysis are color, vascular patterns, and lesion margins, which differentiate between normal and abnormal lesions. This paper provides a review of state-of-the-art image analysis methods to process digital images of the cervix, acquired during VIA procedure for cervical cancer screening of classification of abnormal lesions.

Automation of Detection of Cervical Cancer Using Convolutional Neural Networks.

Classification of digital cervical images acquired during visual inspection with acetic acid (VIA) is an important step in automated image-based cervical cancer detection. Many algorithms have been developed for classification of cervical images based on extracting mathematical features and classifying these images. Deciding the suitability of a feature and learning the algorithm is a complex task. On the other hand, convolutional neural networks (CNNs) self-learn most suitable hierarchical features from the raw input image. In this paper, we demonstrate the feasibility of using a shallow layer CNN for classification of image patches of cervical images as cancerous or not cancerous. We used cervix images acquired after the application of 3%-5% acetic acid using an Android device in 102 women. Of these, 42 cervix images belonged in the VIA-positive category (pathologic) and 60 in the VIA-negative category (healthy controls). A total of 275 image patches of 15 × 15 pixels were manually extracted from VIA-positive areas, and we considered these patches as positive examples. Similarly, 409 image patches were extracted from VIA-negative areas and were labeled as VIA negative. These image patches were classified using a shallow layer CNN composed of a layer each of convolutional, rectified linear unit, pooling, and two fully connected layers. A classification accuracy of 100% is achieved using shallow CNN.

Andriod Device-Based Cervical Cancer Screening for Resource-Poor Settings.

Visual inspection with acetic acid (VIA) is an effective, affordable and simple test for cervical cancer screening in resource-poor settings. But considerable expertise is needed to differentiate cancerous lesions from normal lesions, which is lacking in developing countries. Many studies have attempted automation of cervical cancer detection from cervix images acquired during the VIA process. These studies used images acquired through colposcopy or cervicography. However, colposcopy is expensive and hence is not feasible as a screening tool in resource-poor settings. Cervicography uses a digital camera to acquire cervix images which are subsequently sent to experts for evaluation. Hence, cervicography does not provide a real-time decision of whether the cervix is normal or not, during the VIA examination. In case the cervix is found to be abnormal, the patient may be referred to a hospital for further evaluation using Pap smear and/or biopsy. An android device with an inbuilt app to acquire images and provide instant results would be an obvious choice in resource-poor settings. In this paper, we propose an algorithm for analysis of cervix images acquired using an android device, which can be used for the development of decision support system to provide instant decision during cervical cancer screening. This algorithm offers an accuracy of 97.94%, a sensitivity of 99.05% and specificity of 97.16%.