Breast Cancer Detection through Histological Imaging: A Machine Learning Approach

Journal International Research Journal of Innovations in Engineering and Technology

Breast cancer is one of the most common types of cancer globally, and early detection is crucial for increasing the chances of survival. Mammography and biopsy are conventional diagnostic methods that are accurate but labor-intensive and prone to human error. Recent machine learning (ML)-based advancements have enabled automated systems for cancer classification that may improve their efficiency. In this study, using histological images of size 700 × 460, a total of 1,148 images, the performance of K-Nearest Neighbor (KNN) classification algorithm for breast cancer classification was evaluated. We split the data, where 70% is trained and 30% is tested. To enhance classification accuracy, various data preprocessing methods and feature selection techniques are implemented. The Results show that KNN is offering another fine Performance with Accuracy, Precision, Recall, and F1 score of 100% as the perfect prediction. Choose one optimal k value such that it provides best classification between (benign and malignant cases), which make the KNN one of the most accurate algorithms for breast cancer classification. The study signifies a paradigm shift in medical image analysis, indicating the efficacy of ML-based approaches over traditional approaches.

Enhanced Non-Invasive Blood Glucose Monitoring System Employing Wearable Optical Technology

Diabetes presents significant health risks globally, necessitating precise blood glucose monitoring to prevent serious repercussions including blindness, renal illness, kidney failure, heart disease, and even death from hyperglycemia or hypoglycemia, it is imperative to maintain normal blood glucose levels. However, regular blood glucose monitoring can be difficult for diabetics, and current non-invasive techniques sometimes do not assess blood sugar levels accurately or directly. In order to solve this problem, this study suggests a wearable optical system that is affordable and low-complexity. In this study, a wearable optical system has been proposed which can address the challenges in the accuracy and convenience in existing methods. This system used an Arduino Nano as a central control unit and a laser-transmitted module for blood glucose measurement. Light Dependent Resistors (LDRs) is used to detect and measure the intensity of laser light passing through the skin and impressed by blood glucose levels. The results are displayed on Organic Light Emitting Diode (OLED). During one weak trial, the system achieved average error present of 7.6% and 3.9% for before and after meal blood glucose concentration. The aim of this study is to enhance the lifestyle of diabetic patients by providing user-friendly technology for convenient blood glucose monitoring. It focuses on the potential benefits of non-invasive approaches and concentrates on the importance of the proposed wearable optical system in improving healthcare outcomes

Integrated Artificial Intelligence Approach for Diabetic Foot Ulcer Assessment: A Comprehensive Solution for Precision Diagnosis and Patient Care

Diabetic Foot Ulcers (DFUs) pose significant healthcare challenges, often resulting in severe complications and amputations. Timely and accurate assessment of wound severity is crucial for effective treatment and improved patient outcomes. This study presents an innovative approach to the automated detection of vascular disorders in diabetic feet utilizing infrared thermography. The study employs a dataset comprising 710 thermal images acquired from Al_Wafa Specialized Center for Diabetes and Endocrinology and Bartella General Hospital, which has been meticulously curated for research purposes. To address the specific challenges encountered in diabetic foot imaging and diagnosis, have been introduced three tailored Convolutional Neural Network (CNN) architectures, each designed to identify different risk levels associated with diabetic foot complications. Low-level Diabetic Foot Infection Network (LL-DFI-Net): This architecture is aimed at the detection of low-risk groups, Medium-Level Diabetic Foot Infection Network (ML-DFI-Net) Designed for the medium-risk group, this model targets neuropathic individuals who do not exhibit signs of ischemia. High-Level Diabetic Foot Infection Network (HL-DFI-Net): Focused on the high-risk group, this architecture is specialized for detecting individuals with ischemia. The three networks have achieved high accuracy, as follows: LL-DFI-Net achieved 96.7213%,

Compacted Care Beyond the Clinic: Designing and Implementing a Portable Dental Unit (PDU) for Remote and Mobile Services

For the sake of all those who need dental care in various places, this article presents the rationale for designing and implementing a portable dental unit and finding new and more modern ways to manufacture and evaluate it as well. This device gives priority to durability, portability of the device in different work environments, infection control, and safety. The basic components include, but are not limited to, the air pressure system, the water tank, the Arduino microcontroller to control and manage the pressure, the electrical valves and filters that are subject to international standards, the foot switch, and the device's control panel. The handpiece assembly in this dental unit, which includes the electric motor handle, the triple syringe, the high-speed turbine, and the low-speed turbine, allows for a variety of dental treatments.

Adopting Machine Learning to Automatically Identify a Suitable Surgery Type for Refractive Error Patients

efractive error is a visual impairment that arises when the ocular anatomy hinders the proper focusing of light onto the retina, the light-sensitive tissue layer located at the posterior region of the eye. This condition poses difficulties in achieving clear vision. Refractive error stands as the prevailing kind of visual impairment. The objective of this study is to classify two surgical approaches utilized in the treatment of refractive defects. Two commonly performed refractive surgeries are Photo-Refractive Keratectomy (PRK) and Laser-Assisted In-Situ Keratomileusis (LASIK). Artificial Intelligence (AI) encompasses a specific branch known as Machine Learning (ML), which is the focal point of this investigation. ML is dedicated to the advancement and use of algorithms that possess the capacity to acquire knowledge from data and enhance their predictive capabilities without explicit programming. The present study employs sophisticated ML methods to classify different types of refractive defect surgeries using a dataset of 124 samples obtained from Al-Rabee Hospital in Iraq, specifically focusing on corneal topography data. Two ML approaches, namely K-Nearest Neighbor (KNN) and Artificial Neural Network (ANN), are employed to predict the kind of refractive defect surgery. The findings produced from the experiment demonstrated an accuracy rate of 90.32% for the KNN algorithm and a perfect accuracy rate of 100% for the ANN algorithm. Additionally, the KNN algorithm exhibited a sensitivity of 90% and a specificity of 90.54%. The study’s findings indicate that the ANN classifier outperforms the KNN classifier.

Exploring CIE Lab Color Characteristics for Skin Lesion Images Detection: A Novel Image Analysis Methodology Incorporating Color-based Segmentation and Luminosity Analysis

Accurate classification of malignant and benign skin lesions is crucial in dermatology. In this novel research, we propose robust image analysis methodology for skin lesion classification that integrates color-based segmentation with luminosity analysis. Our approach is evaluated on a dataset of 400 skin images, with equal representation of malignant and benign samples. By computing mean color values for the Red Channel Color (RCC), Green Channel Color (GCC), and Blue Channel Color (BCC) in groups of 10 samples, we establish a classification range for precise diagnosis, this research introduces a novel dimension by harnessing the potential of the CIE Lab Color characteristics for skin lesion detection as the most reliable scale for distinguishing between benign and malignant samples. The smaller and more thought variety ranges saw in the glow examination improve difference and perceivability, consequently working with prevalent sore separation. By featuring the meaning of mean histograms for each variety channel, this complete exploration adds to propelling the area of dermatology and presents an imaginative methodology that holds guarantee for PC helped conclusion frameworks in skin malignant growth discovery

An Artificial Intelligence Approach for Verifying Persons by Employing the Deoxyribonucleic Acid (DNA) Nucleotides

Journal Journal of Electrical and Computer Engineering

Deoxyribonucleic acid (DNA) can be considered as one of the most useful biometrics. It has effectively been used for recognizing persons. However, it seems that there is still a need to propose a new approach for verifying humans, especially after the recent big wars, where too many people lost and die. This approach should have the capability to provide high personal verification performance. In this paper, a personal recognition approach based on artificial intelligence is proposed. This approach is called the artificial DNA algorithm for recognition (ADAR). It utilizes a unique identity for each person acquired from DNA nucleotides, and it can verify individuals efficiently with high performance. The ADAR has been designed and applied to multiple datasets, namely, the DNA classification (DC), sample DNA sequence (SDS), human DNA sequences (HDS), and DNA sequences (DS). For all datasets, a low value of 0% is achieved for each of the false acceptance rate (FAR) and false rejection rate (FRR).

Healthcare Monitoring COVID-19 Patients Based on IoT System

At the beginning of the Coronavirus disease 2019 (COVID-19) pandemic, the world needed to develop an innovative, accurate system for caring for and following up with patients remotely to reduce the massive influx of patients into hospitals. Therefore, the well-established Internet of Things (IoT) technology was used to build an applied model for health care. The main objective of this study was to create a system connected to an application that allows continuous remote and early detection of clinical deterioration by monitoring different levels of biometrics to reduce the patient's risk of serious complications. Assessments were conducted on four subjects (two males, two females) aged 30-50 years with COVID-19. The system was examined under conditions and medical supervision in the hospital, following a schedule of vital measurements (oxygen saturation rate, heart rate and temperature). An average of 4 examinations was recorded per day over a week. The model has recorded the mean of error of oxygen saturation rate (SpO2), pulse rate, and body temperature as (0.3975%), (0.2625%) and (2.925%) for four patients.

Healthcare Monitoring COVID-19 Patients Based on IoT System

At the beginning of the Coronavirus disease 2019 (COVID-19) pandemic, the world needed to develop an innovative, accurate system for caring for and following up with patients remotely to reduce the massive influx of patients into hospitals. Therefore, the well-established Internet of Things (IoT) technology was used to build an applied model for health care. The main objective of this study was to create a system connected to an application that allows continuous remote and early detection of clinical deterioration by monitoring different levels of biometrics to reduce the patient's risk of serious complications. Assessments were conducted on four subjects (two males, two females) aged 30-50 years with COVID-19. The system was examined under conditions and medical supervision in the hospital, following a schedule of vital measurements (oxygen saturation rate, heart rate and temperature). An average of 4 examinations was recorded per day over a week. The model has recorded the mean of error of oxygen saturation rate (SpO2), pulse rate, and body temperature as (0.3975%), (0.2625%) and (2.925%) for four patients.

Using IoT technology for monitoring Alzheimer's and elderly patients

Alzheimer's disease (AD) is a neurological disorder that results in the death of brain cells, causing memory loss, behavioral changes, and cognitive impairment. It drastically affects the individual's work and social life, often leading to death, and is now the sixth leading cause of mortality worldwide. AD patients have limited mobility, which restricts their movement outside their homes. Thankfully, new internet of things (IoT) applications have made it possible to monitor people with various illnesses in their everyday lives, providing valuable assistance to caregivers. This study aims to create an IoT prototype that can locate an AD patient in real time and remind them to take their medication on schedule via an alarm. The small, lightweight, portable patient carrier has a NodeMCU-23DSP board, a Neo-06 global positioning system (GPS) module, and a wireless modem/Wi-Fi router. Remote patient follow-up through the Blynk 2.0 application on computers and Android devices allows for monitoring of the patient's medication regimen and daily activities. As AD patients struggle with memory and organization, the prototype's design enables monitoring of a patient's course of medication, making it easier for caregivers to provide the necessary assistance. The prototype was tested to demonstrate its efficiency and performance.

Classification ECG Signals Base on kNearest Neighbors (k-NN) Algorithm

Abnormal cardiac rhythm known as atrial fibrillation (AF) is marked by an atria's fast and erratic pulse. It often begins in short periods of abnormal beating which becomes longer and may be constant over time. Usually it presents no symptoms and a typical ECG affected by Atrial Fibrillation does not present any P wave and shows an irregular ventricular rate. In this study, the k-Nearest Neighbors (K-NN) algorithm has been used to classifier 5000 samples of cardiac signals. After preprocessing the data, it was split into the three classes represented, namely: Normal (N), AF, and Noisy Rhythm (NR). In a ratio of 1:1, the data were split into two groups: training dataset and test dataset, to perform the classification. It was obtained from the dataset, the highest sensitivity recorded for N cases is 92% and the highest specificity recorded for AF is 99%. The classification accuracy obtained is 90% and the value for area under the curve (AUC) is 0.94

Clinical Fusion for Real-Time Complex QRS Pattern Detection in Wearable ECG Using the Pan-Tompkins Algorithm

DOI DOI: 10.54216/FPA.120214

This scientific paper presents a novel approach of real-time signal analysis in electrocardiogram (ECG) monitoring systems, focusing on the integration of device design,algorithm implementation for accurate measurement and interpretation of heart activity. The proposed system leverages a low-cost framework, employing a microcontroller and Arduino programming language for raw ECG data acquisition, while utilizing the AD8232 sensor and ESP8266 Node MCU for continuous patient monitoring. The acquired data is processed, stored, and analyzed using the Pan-Tompkins algorithm, which effectively filters and analyzes heart signals, including noise reduction and QRS complex detection. Two case studies involving a healthy individual and a patient with Myocarditis were conducted to demonstrate the effectiveness of the system. The integration of device design and algorithm development in ECG analysis is emphasized, highlighting the affordability, wearability, and potential for continuous monitoring and early detection of heart conditions. By successfully mitigating noise-related challenges, the implementation of the Pan algorithm enables accurate signal analysis. This interdisciplinary research contributes to the advancement of ECG interpretation and underscores the significance of clinical fusion between designed systems and applied algorithms on real cases. The performance of two Pan-Tompkins based QRS complex detection algorithms was systematically analyzed, offering valuable insights for their reasonable utilization.

Skin Cancer Detection Using K-Means Clustering-Based Color Segmentation

Approximately 75% of all cancers are found on the skin. Due to its high mortality rate, Skin cancer (SC) must be treated immediately after detection. SC, in fact, results from abnormalities in the skin's surface. In spite of the fact that most people who have SC make a full recovery, this disease remains a major source of anxiety for the general public. Most SCs develop only locally and invade surrounding tissues, but melanoma, the rarest form of SC, may move throughout the body via the bloodstream and lymphatic system. in this study k mean algorithm and color space has been used to detect melanoma skin cancer. The data set used has been obtained from Kaggle skin cancer collection challenge. The input image has been changed to color space, k mean clustering algorithm was used to cluster the image into three clusters and return an index corresponding to each cluster then the a’b’ layers have been used as image clusters and l layer has been used to detect the exact part of the image.

Hematological Classification of White Blood Cells by Exploiting Digital Microscopic Images

A blood test is an essential examination process for evaluating body functions. Blood cell classification is an important laboratory process for detecting blood diseases. Microscopic evaluation by experts is a slow process, and the outcome depends on skill and experience. In addition, the process can be tedious and time-consuming. Therefore, an automated medical diagnostic system is essential in recognizing diseases in a short time and providing information about blood-related diseases such as leukemia. White blood cells are one of the most important types of blood cells associated with the immune system, as their forms are important and necessary for diagnosing blood diseases. In this study, a new Deep Learning (DL) network model called White Blood Cell Hematological Diseases Classification (WBC_HDC) is proposed. The classification was based on a convolution neural network (CNN) scheme for classifying hematology. A dataset containing 2800 images of white blood cells has been used, which were obtained with a CellaVision DM96 analyzer in the laboratory of the Barcelona Hospital Clinic (BHC). The data set is organized into the following four groups: Lymphocytes, Monocytes, Immature Granulocytes (IG), and Erythroblasts. The images' sizes are 360 x 363x 3 pixels in Joint Photographic Group (JPG ) format and have been annotated by clinical pathology experts. Images were taken from individuals without infection, hematology, or neoplasia and free of any drug treatment at the moment of blood collection. The WBC_HDC network has recorded an accuracy of 90.86% after going through numerous tests of network layer parameters for eight different stages.

Classifying healthy and infected Covid-19 cases by employing CT scan images

Journal Bulletin of Electrical Engineering and Informatics

broad family of viruses called coronaviruses may infect people. The infection's symptoms are often relatively minor and resemble a normal cold. Since the coronavirus disease of 2019 (Covid-19) has never been observed in humans, anyone can contract it, and no one has an innate immunity to it. The detection of Covid-19 is now a critical task for medical practitioners. computed tomography (CT) scans can be considered as the best way to diagnose Covid-19. For patients with severe symptoms, imaging might help to assess the seriousness of the disease. Also, the CT scan can be helpful for determining a plan of care for a patient. This work focuses on classifying Covid-19 cases for healthy and infected by presenting a powerful scheme of recognizing CT scan images. In this study will be provided by proposing a model based on applying deep feature extractions with support vector machine (SVM). Big dataset of CT scan images is employed, it is available in the repository of GitHub and Kaggle. Remarkable result of 100% have been benchmarked as the highest evaluation after investigations. The proposed model can automatically detect between healthy and infected individuals

Innovative Non-Invasive Blood Sugar Level Monitoring for Diabetes Using UWB Sensor

Globally, hundred millions of person are affected of diabetes, whether Type-1-or Type-2-, affects people all over the world, and diabetes is a leading cause of mortality in many nations. Regular the level of sugar in the blood in both types assist to reduce the danger of diabetes hyperglycemia (blood sugar levels > 200 mg/dL) or hypoglycemia (blood sugar levels < 70 mg/dL) problems, for example, Kidney failure, heart problems, blindness, and even death are all possibilities. They need frequently check the sugar level of blood in a day. This is one of the most crucial challenges that diabetics often face to take a blood sample and measure the sugar level daily. Numerous non-invasive methods have been proposed to solve this problem. Most previous articles are inaccuracy and most of these studies independent on blood directly. The objectives of this research are to design and implement a prototype of a wearable (UWB) non-invasive system that can prevent infections with low cost and low complexity, to minimize the error in readings of patients when they use invasive or non-invasive ways to improve the lifestyle and To find a certain technique that can be used in vast field with the patient himself at any time. The proposed UWB has been achieved an integrated easy, flexible, wearable and good accuracy. The UWB outperformed previously implemented systems shown in earlier works, both in terms of easy to use and low cost.

CLASSIFYING VARIOUS BRAIN ACTIVITIES BY EXPLOITING DEEP LEARNING TECHNIQUES AND GENETIC ALGORITHM FUSION METHOD

The scan of functional Magnetic Resonance Imaging (fMRI) can provide three views for brain activities. These views are basically the X_axis (sagittal Plane), Y_axis (coronal plane) and Z_axis (axial plane). To the best of the obtained knowledge, studying brain activities for all of these views has not been considered before together with Deep Learning (DL) techniques. In this paper, various DL models named the X_axis Classification Model (XCM), Y_axis Classification Model (YCM) and Z_axis Classification Model (ZCM) are proposed. Each of these models is able to classify between the vision, movement and forward brain activities. Extensive experiments are performed for examining their parameters. The designed models have the capability to automatically detect the important features without any human supervision. In addition, they can provide intelligent decisions or classifications. Furthermore, effective combination method is suggested based on the Genetic Algorithm (GA) and Genetic Weighted Summation (GWS) rule, where high performances of outcomes can be achieved. After extensive experiments, the accuracies of 91.67%, 89.88% and 91.67% have been obtained for the XCM, YCM and ZCM, respectively. In addition, the accuracy has been raised to 97.22% by applying the suggested fusion method.

COMPARISON STUDY BETWEEN THREE AXIS VIEWS OF VISION, MOTOR AND PRE-FRONTAL BRAIN ACTIVITIES

The brain is the main controller of the vital processes and functional studies. The Functional Magnetic Resonance Imaging (fMRI) can provide useful indications to explain the functional activities of brain. Especially with utilizing the three image views of X_axis (Sagittal Plane) split the brain into left and right segment, Y_axis (Coronal plane) split the brain into back and front segment and Z_axis (Axial plane) split the brain into top and down segment. In this study, we proposevarious deep learning models namely the X_axis Classification Model (XCM), Y_axis Classification Model (YCM) and Z_axis Classification Model (ZCM) for classify three tasks of vision, movement and pre-frontal brain activities. In addition of presenting comparisons between different network models for all of the three views. After extensive experiments and comparisons even with state-of-the-art studies, promising accuracies of 91.67%, 89.88% and 91.67% have successfully been obtained for the XCM, YCM and ZCM, respectively.