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.

Automated Decision Support System for Detection of Leukemia from Peripheral Blood Smear Images.

Peripheral blood smear analysis plays a vital role in diagnosing many diseases including cancer. Leukemia is a type of cancer which begins in bone marrow and results in increased number of white blood cells in peripheral blood. Unusual variations in appearance of white blood cells indicate leukemia. In this paper, an automated method for detection of leukemia using image processing approach is proposed. In the present study, 1159 images of different brightness levels and color shades were acquired from Leishman stained peripheral blood smears. SVM classifier was used for classification of white blood cells into normal and abnormal, and also for detection of leukemic WBCs from the abnormal class. Classification of the normal white blood cells into five sub-types was performed using NN classifier. Overall classification accuracy of 98.8% was obtained using the combination of NN and SVM.

Computerized Morphometric Analysis of Eryptosis.

Eryptosis is the suicidal destruction-process of erythrocytes, much like apoptosis of nucleated cells, in the course of which the stressed red cell undergoes cell-shrinkage, vesiculation and externalization of membrane phosphatidylserine. Currently, there exist numerous methods to detect eryptosis, both morphometrically and biochemically. This study aimed to design a simple but sensitive, automated computerized approach to instantaneously detect eryptotic red cells and quantify their hallmark morphological characteristics. Red cells from 17 healthy volunteers were exposed to normal Ringer and hyperosmotic stress with sodium chloride, following which morphometric comparisons were conducted from their photomicrographs. The proposed method was found to significantly detect and differentiate normal and eryptotic red cells, based on variations in their structural markers. The receiver operating characteristic curve analysis for each of the markers showed a significant discriminatory accuracy with high sensitivity, specificity and area under the curve values. The software-based technique was then validated with RBCs in malaria. This model, quantifies eryptosis morphometrically in real-time, with minimal manual intervention, providing a new window to explore eryptosis triggered by different stressors and diseases and can find wide application in laboratories of hematology, blood banks and medical research.

Image Processing Approach for Detection of Leukocytes in Peripheral Blood Smears.

Peripheral blood smear analysis is a gold-standard method used in laboratories to diagnose many hematological disorders. Leukocyte analysis helps in monitoring and identifying health status of a person. Segmentation is an important step in the process of automation of analysis which would reduce the burden on hematologists and make the process simpler. The segmentation of leukocytes is a challenging task due to variations in appearance of cells across the slide. In the proposed study, an automated method to detect nuclei and to extract leukocytes from peripheral blood smear images with color and illumination variations is presented. Arithmetic and morphological operations are used for nuclei detection and active contours method is for leukocyte detection. The results demonstrate that the proposed method detects nuclei and leukocytes with Dice score of 0.97 and 0.96 respectively. The overall sensitivity of the method is around 96%.

Feature extraction using traditional image processing and convolutional neural network methods to classify white blood cells: a study.

White blood cells play a vital role in monitoring health condition of a person. Change in count and/or appearance of these cells indicate hematological disorders. Manual microscopic evaluation of white blood cells is the gold standard method, but the result depends on skill and experience of the hematologist. In this paper we present a comparative study of feature extraction using two approaches for classification of white blood cells. In the first approach, features were extracted using traditional image processing method and in the second approach we employed AlexNet which is a pre-trained convolutional neural network as feature generator. We used neural network for classification of WBCs. The results demonstrate that, classification result is slightly better for the features extracted using the convolutional neural network approach compared to traditional image processing approach. The average accuracy and sensitivity of 99% was obtained for classification of white blood cells. Hence, any one of these methods can be used for classification of WBCs depending availability of data and required resources.

Development of a Robust Algorithm for Detection of Nuclei and Classification of White Blood Cells in Peripheral Blood Smear Images.

Peripheral Blood Smear analysis plays a vital role in diagnosis of many diseases such as leukemia, anemia, malaria, lymphoma and infections. Unusual variations in color, shape and size of blood cells indicate abnormal condition. We used a total of 117 images from Leishman stained peripheral blood smears acquired at a magnification of 100X. In this paper we present a robust image processing algorithm for detection of nuclei and classification of white blood cells based on features of the nuclei. We used novel image enhancement method to manage illumination variations and TissueQuant method to manage color variations for the detection of nuclei. Dice similarity coefficient of 0.95 was obtained for nucleus detection. We also compared the proposed method with a state-of-the-art method and the proposed method was found to be better. Shape and texture features of the detected nuclei were used for classifying white blood cells. We considered classification of WBCs using two approaches such as 5-class and cell-by-cell approaches using neural network and hybrid-classifier respectively. We compared the results of both the approaches for classification of white blood cells. Cell-by-cell approach offered 1.4% higher sensitivity in comparison with the 5-class approach. We obtained an accuracy of 100% for lymphocyte and basophil detection. Hence, we conclude that lymphocytes and basophils can be accurately detected even when the analysis is limited to the features of nuclei whereas, accurate detection of other types of WBCs will require analysis of the cytoplasm too.

Co-Infection: Weil's Syndrome with Hepatitis B Infection- A Diagnostic and Therapeutic Hitch.

Undifferentiated Acute Febrile Illness (AFI) is a common clinical syndrome among patients seeking hospital care. Detection of co-infections at the time of presentation is a diagnostic challenge, especially with limited laboratory support. Even if detected, early treatment and cure of these co-infections can be difficult for the clinicians. We are presenting a rare case of Hepatitis B and leptospirosis co-infection with high titres of Salmonella paratyphi A and scrub typhus. There are a few reports of leptospirosis in Hepatitis -B infected individuals but no generalization can be made due to limited data. Prompt and accurate serological diagnosis of multiple infectious agents have becomes mandatory in a healthcare set-up.

Salmonella Paratyphi B septicemia in a Neonate.

Septicemia is a major cause of death in neonates especially in developing countries. We report a case of septicemia in a neonate due to Salmonella Paratyphi B. The baby responded well to therapy and recovered completely.