The second largest cause of mortality worldwide is breast cancer, and it mostly occurs in women. Early diagnosis has improved further treatments and reduced the level of mortality. A unique deep learning algorithm is presented for predicting breast cancer in its early stages. This method utilizes numerous layers to retrieve significantly greater amounts of information from the source inputs. It could perform automatic quantitative evaluation of complicated image properties in the medical field and give greater precision and reliability during the diagnosis. The dataset of axillary lymph nodes from the breast cancer patients was collected from Erasmus Medical Center. A total of 1050 images were studied from the 850 patients during the years 2018 to 2021. For the independent test, data samples were collected for 100 images from 95 patients at national cancer institute. The existence of axillary lymph nodes was confirmed by pathologic examination. The feed forward, radial basis function, and Kohonen self-organizing are the artificial neural networks (ANNs) which are used to train 84% of the Erasmus Medical Center dataset and test the remaining 16% of the independent dataset. The proposed model performance was determined in terms of accuracy (Ac), sensitivity (Sn), specificity (Sf), and the outcome of the receiver operating curve (Roc), which was compared to the other four radiologists' mechanism. The result of the study shows that the proposed mechanism achieves 95% sensitivity, 96% specificity, and 98% accuracy, which is higher than the radiologists' models (90% sensitivity, 92% specificity, and 94% accuracy). Deep learning algorithms could accurately predict the clinical negativity of axillary lymph node metastases by utilizing images of initial breast cancer patients. This method provides an earlier diagnostic technique for axillary lymph node metastases in patients with medically negative changes in axillary lymph nodes.

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Deep Learning Mechanism for Predicting the Axillary Lymph Node Metastasis in Patients with Primary Breast Cancer

Diabetic patients can also be identified immediately utilizing retinopathy photos, but it is a challenging task. The blood veins visible in fundus photographs are used in several disease diagnosis approaches. We sought to replicate the findings published in implementation and verification of a deep learning approach for diabetic retinopathy identification in retinal fundus pictures. To address this issue, the suggested investigative study uses recurrent neural networks (RNN) to retrieve characteristics from deep networks. As a result, using computational approaches to identify certain disorders automatically might be a fantastic solution. We developed and tested several iterations of a deep learning framework to forecast the progression of diabetic retinopathy in diabetic individuals who have undergone teleretinal diabetic retinopathy assessment in a basic healthcare environment. A collection of one-field or three-field colour fundus pictures served as the input for both iterations. Utilizing the proposed DRNN methodology, advanced identification of the diabetic state was performed utilizing HE detected in an eye's blood vessel. This research demonstrates the difficulties in duplicating deep learning approach findings, as well as the necessity for more reproduction and replication research to verify deep learning techniques, particularly in the field of healthcare picture processing. This development investigates the utilization of several other Deep Neural Network Frameworks on photographs from the dataset after they have been treated to suitable image computation methods such as local average colour subtraction to assist in highlighting the germane characteristics from a fundoscopy, thus, also enhancing the identification and assessment procedure of diabetic retinopathy and serving as a skilled guidelines framework for practitioners all over the globe.

https://downloads.hindawi.com/journals/bmri/2022/3163496.pdf

A Deep Learning Framework for Earlier Prediction of Diabetic Retinopathy from Fundus Photographs

As an epidemic, COVID-19's core test instrument still has serious flaws. To improve the present condition, all capabilities and tools available in this field are being used to combat the pandemic. Because of the contagious characteristics of the unique coronavirus (COVID-19) infection, an overwhelming comparison with patients queues up for pulmonary X-rays, overloading physicians and radiology and significantly impacting the quality of care, diagnosis, and outbreak prevention. Given the scarcity of clinical services such as intensive care and motorized ventilation systems in the aspect of this vastly transmissible ailment, it is critical to categorize patients as per their risk categories. This research describes a novel use of the deep convolutional neural network (CNN) technique to COVID-19 illness assessment seriousness. Utilizing chest X-ray images as contribution, an unsupervised DCNN model is constructed and suggested to split COVID-19 individuals into four seriousness classrooms: low, medium, serious, and crucial with an accuracy level of 96 percent. The efficiency of the DCNN model developed with the proposed methodology is demonstrated by empirical findings on a suitably huge sum of chest X-ray scans. To the evidence relating, it is the first COVID-19 disease incidence evaluation research with four different phases, to use a reasonably high number of X-ray images dataset and a DCNN with nearly all hyperparameters dynamically adjusted by the variable selection optimization task.

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Deep Convolutional Neural Network Mechanism Assessment of COVID-19 Severity

Prenatal heart disease, generally known as cardiac problems (CHDs), is a group of ailments that damage the heartbeat and has recently now become top deaths worldwide. It connects a plethora of cardiovascular diseases risks to the urgent in need of accurate, trustworthy, and effective approaches for early recognition. Data preprocessing is a common method for evaluating big quantities of information in the medical business. To help clinicians forecast heart problems, investigators utilize a range of data mining algorithms to examine enormous volumes of intricate medical information. The system is predicated on classification models such as NB, KNN, DT, and RF algorithms, so it includes a variety of cardiac disease-related variables. It takes do with an entire dataset from the medical research database of patients with heart disease. The set has 300 instances and 75 attributes. Considering their relevance in establishing the usefulness of alternate approaches, only 15 of the 75 criteria are examined. The purpose of this research is to predict whether or not a person will develop cardiovascular disease. According to the statistics, naïve Bayes classifier has the highest overall accuracy.

/pdf/an-image-processing-approach-for-detection-of-prenatal-heart-qb8jjuqr.pdf

An Image Processing Approach for Detection of Prenatal Heart Disease

In women, cervical cancer is a commonly occurring more dangerous cancer. 90% of the women suffer from this cancer. The cancer begins in thin, flat, flat epithelial cells on the surface of the external neck of the cervix. With the early detection it is curable. But diagnosis is complicated. To identify the cancer nowadays machine learning algorithms, deep learning algorithms, fuzzy logics and artificial intelligence are used. In this proposed system, to identify the cancer cells, the gray scale images were used. The texture is analyzed with a Gabor filter and high impact features were identified with histogram equalization. SVM used for the classification of cancer and non-cancer cells. The obtained accuracy is 97%.

Detection of Cervical Cancer with Texture Analysis using Machine Learning Models

Hospital data management is one of the functional parts of operations to store and access healthcare data. Nowadays, protecting these from hacking is one of the most difficult tasks in the healthcare system. As the user’s data collected in the field of healthcare is very sensitive, adequate security measures have to be taken in this field to protect the networks. To maintain security, an effective encryption technology must be utilised. This paper focuses on implementing the elliptic curve cryptography (ECC) technique, a lightweight authentication approach to share the data effectively. Many researches are in place to share the data wirelessly, among which this work uses Electronic Medical Card (EMC) to store the healthcare data. The work discusses two important data security issues: data authentication and data confidentiality. To ensure data authentication, the proposed system employs a secure mechanism to encrypt and decrypt the data with a 512-bit key. Data confidentiality is ensured by using the Blockchain ledger technique which allows ethical users to access the data. Finally, the encrypted data is stored on the edge device. The edge computing technology is used to store the medical reports within the edge network to access the data in a very fast manner. An authenticated user can decrypt the data and process the data at optimum speed. After processing, the updated data is stored in the Blockchain and in the cloud server. This proposed method ensures secure maintenance and efficient retrieval of medical data and reports.

/pdf/ensuring-improved-security-in-medical-data-using-ecc-and-4bfw20v7wk.pdf

Ensuring Improved Security in Medical Data Using ECC and Blockchain Technology with Edge Devices

\n Glaucoma is a major threatening cause, in which it affects the optical nerve to lead a permanent blindness to individuals. The major causes of Glaucoma are high pressure to eyes, family history, irregular sleeping habits and so on. These kinds of causes leads to Glaucoma easily as well as the affection to such disease leads a heavy damage to the internal optic nervous system and the affected person will get permanent blindness within few months. The eye fluid called aqueous humor is getting blocked inside due to Glaucoma, in normal cases sometimes the fluid comes out from the eye via mesh perspective channel, but this Glaucoma blocks that channel and causes the fluid to getting locked inside and provides the permanent blockage inside. So, that the eyes are getting severe affections such as infection, random blindness in initial stages and so on. The World Health Organization analyzes and reports nearly 80 million people around the globe are affected due to some form of Glaucoma. The major problem with this disease is it is incurable, however, the affection stages can be reduced and maintain the same level of affection as it is for the long period but it is possible only earlier stages of identification. This Glaucoma causes structural affection to the eye ball and it is complex to estimate the cause during regular diagnosis. In medical terms, the Cup to Disc Ratio (CDR) is minimized to the Glaucoma patients suddenly and leads a harmful damage to one's eye in severe manner. The general way to identify the Glaucoma is to take Optical Coherence Tomography (OCT) test, in which it captures the uncovered portion of eye ball (backside) and it is an efficient way to visualize diverse portions of eyes with optical nerve visibility is shown clearly. The OCT images are mainly used to identify the diseases like Glaucoma with proper and robust accuracy levels. In this paper, a new methodology is introduced to identify the Glaucoma on earlier stages called Depth Optimized Machine Learning Strategy (DOMLS), in which it adapts the new optimization logic called Modified K-Means Optimization Logic (MkMOL) to provide best accuracy in results and the proposed approach assures the accuracy level of more than 96.2% with least error rate of 0.002%. This paper focuses on the identification of early stage of Glaucoma and provides an efficient solution to people in case of affection by such disease using OCT images. The exact position point out is handled by using Region of Interest (ROI) based optical region selection, in which it is easy to point the Optical Cup (OC) and Optical Disc (OD). The proposed algorithm of DOMLS proves the accuracy levels in estimation of Glaucoma and shows the practical proofs on resulting section in clear manner.

/pdf/energetic-glaucoma-segmentation-and-classification-1sz05vqefx.pdf

Energetic Glaucoma Segmentation and Classification Strategies using Depth Optimized Machine Learning Strategies

An intelligent disease prediction and monitoring system using feature selection, multi-neural network and fuzzy rules

The vast amount of data available on social media and microblogs can be a valuable resource for mining opinions or for analyzing the overall mood of the public. This helps in identifying potential customers, exploring market trends and predicting events. Analyzing twitter data is comparatively difficult due to the large amount of irregularities present in tweets. Many approaches that use sentiment dictionaries and machine learning have been proposed until now. In this paper, we present a new feature that is extracted using dependency parsing and an emotion lexicon. This feature, along with n-grams, syntactic n-grams and lexicon-based features, is used to classify the tweets. We also use custom dictionaries to identify slang words, SMS short forms, emoticons and word contractions. The performance of various classification algorithms and ensemble techniques is compared. Our results show that the new feature along with the ensemble framework improves sentiment classification.

Analysis of Ensemble Classification of Twitter Sentiments Using New Dependency Tree Based Approach

In this project, we focus on electronic mail, one of the most important means of communication among information professionals. As its use and significance among the general populace grows, so does its importance and utility. It has allowed for more adaptability and convenience in communication, both in the private and professional spheres. The increased use of email has led to a rise in spam as well as legitimate messages. An email that is sent to a large number of people without the sender’s knowledge or consent is considered spam. Millions of internet users, both casual and professional, are currently frustrated by the widespread problem of email spam. The purpose of this study is to provide a hybrid approach to machine learning for identifying spam in email. Bagging and boosting of machine learning-based multinomial Decision Tree, Naive Bayes, KNN, Random Forest, and the SVM method are the proposed hybrid techniques. The bagging method uses a concurrent combination of weak classifiers to boost classification accuracy. The standard deviation of misclassifications is decreased by using bagging. Alternatively, by linking the classifiers in a series fashion, the boosting strategy can construct a robust classifier out of two or more relatively weak classifiers. Improved classification results can be achieved through reduced bias and variance thanks to the use of boosting. In order to detect spam in emails, it is necessary to take into account datasets, pre-process those datasets, extract and pick features, and classify the data. In this study, we evaluate the feasibility of conducting experiments using data from the Ling-Spam Corpus and the CSDMC2010 Spam Corpus. According to the stop-word list and lemmatiser, Ling-Spam Corpus’s dataset is split into four different directories: bare, lemm, lemm stop, and stop. In addition, pre-processing consists of converting strings to word vectors (tokenization), stemming words, and removing stop words. Since the Ling Spam Corpus is already organised according to the stop-word list and the lemmatiser, only the CSDMC2010 Spam Corpus undergoes the stemming and stop words removal processes. Features are extracted and selected from the preprocessed data. The feature selection procedure in this work makes use of a correlation-based approach.

V. Elizabeth Jesi

Papers

Ensuring Improved Security in Medical Data Using ECC and Blockchain Technology with Edge Devices

Energetic Glaucoma Segmentation and Classification Strategies using Depth Optimized Machine Learning Strategies

An intelligent disease prediction and monitoring system using feature selection, multi-neural network and fuzzy rules

Analysis of Ensemble Classification of Twitter Sentiments Using New Dependency Tree Based Approach

Spam Email Detection Using Machine Learning Integrated In Cloud