Malaria, a life-threatening disease worldwide, can be diagnosed using antigen tests and microscopy tests. However, both of them are erroneous and time-consuming. Therefore, a trustworthy and fast early malaria prognosis infrastructure is required. In this age of machine learning (ML), there are several ML-based methods to do the task. This paper proposes an unorthodox method for malaria prognosis based on an extreme learning machine (ELM) algorithm. In this regard, Convolutional Neural Networks (CNN), ELM, and double hidden layer (DELM) have been used as classifiers. A CNN model has been used as a feature extractor and also as a classifier to perform a comparative study. The derived features have been used to train ELM and DELM. Two versions of the malaria image dataset have been used: one is the original dataset, and the other is a modified dataset where ambiguous samples have been removed. The parasite inflator acts as the shape increaser of the small, darker malaria parasites in the RBC images in order to detect malaria easily. CNN-DELM has achieved a sanguine result on every performance standard compared to CNN and CNN-ELM. The proposed CNN-DELM method has achieved 97.79% and 99.66% accuracy for the original version and the modified version, respectively. Hence, the proposed CNN-DELM model has also produced either comparable or better results when compared to other methods proposed in the literature, showing its robustness in detecting malaria. 

/pdf/diagnosis-of-malaria-using-double-hidden-layer-extreme-2zkouvii.pdf

Diagnosis of Malaria Using Double Hidden Layer Extreme Learning Machine Algorithm With CNN Feature Extraction and Parasite Inflator

A significant health issue, cancer is becoming more prevalent globally and is a leading cause of mortality. Recent studies have shown that breast cancer is one of the most prevalent cancer type, particularly among women. Early detection can increase the chances of survival for those with breast cancer and lower treatment cost. However, there are drawbacks to the early diagnosis methods utilized in today’s healthcare systems. These include the need for substantial human resources, long-term effects, and difficult access to these services for everybody. For early breast cancer diagnosis, technologies that are simple to use, yield reliable findings compared to scientific methodologies, and are available to everyone are required. Artificial Intelligence techniques enable the early diagnosis of breast cancer. This study aims to classify benign and malignant breast cancer image features. Artificial Neural Network, Support Vector Machine and Random Forest algorithms were used to classify features obtained from images. Experiments were performed on the Wisconsin Breast Cancer dataset. Experimental evaluation shows that 99% of the most successful results were achieved with the Artificial Neural Network algorithm. According to experimental findings, the classification technique can identify breast cancer in its early stages. The findings of the study are expected to shed on light new researches for investigation into breast cancer early detection. 

Performance Comparison of Different Machine Learning Techniques for Early Prediction of Breast Cancer using Wisconsin Breast Cancer Dataset

A Review on Computational Methods for Breast Cancer Detection in Ultrasound Images Using Multi-Image Modalities

A significant health issue, cancer is becoming more prevalent globally and is a leading cause of mortality. Recent studies have shown that breast cancer is one of the most prevalent cancer type, particularly among women. Early detection can increase the chances of survival for those with breast cancer and lower treatment cost. However, there are drawbacks to the early diagnosis methods utilized in today’s healthcare systems. These include the need for substantial human resources, long-term effects, and difficult access to these services for everybody. For early breast cancer diagnosis, technologies that are simple to use, yield reliable findings compared to scientific methodologies, and are available to everyone are required. Artificial Intelligence techniques enable the early diagnosis of breast cancer. This study aims to classify benign and malignant breast cancer image features. Artificial Neural Network, Support Vector Machine and Random Forest algorithms were used to classify features obtained from images. Experiments were performed on the Wisconsin Breast Cancer dataset. Experimental evaluation shows that 99% of the most successful results were achieved with the Artificial Neural Network algorithm. According to experimental findings, the classification technique can identify breast cancer in its early stages. The findings of the study are expected to shed on light new researches for investigation into breast cancer early detection.

Breast cancer is the one common cause of death in both developed worlds and the most death-causing disease diagnosed among women. Early recognition of this condition can help to minimize death rates. The breast problem statement, in brief, is not reliable for accuracy recognition. They have a high degree of classification accuracy as well as diagnostic capabilities. The most common classifications are normal, benign cancer, and malignant cancer. Machine learning (ML) techniques are now widely used in the classification of breast cancer. In this paper, some machine learning technics have been investigated to diagnose breast cancer (BC) on magnetic resonance imaging (MRI) images using multi-step processes. The first step has been to take the MRI image as an input image and have been pre-processing an image, then use feature extraction by using (scale-invariant feature transform (SIFT), histogram of oriented gradient (HOG), local binary patterns (LBP), bag of words (BoW), and edge-oriented histogram (EOH)). Next step we implement the classifying algorithms (KNN, decision tree (DT), naïve Bayes, ANN, SVM, RF, AdaBoost), have been used to detect and classify the normal or breast cancer region for this purpose datasets like ACRIN-Contralateral-Breast-MRI, In and breast cancer MRI dataset) has been collected our breast cancer MRI images from Erbil and Sulaymaniyah hospital the results was 91.9%, the result of ACRIN was 97% and the results Breast Cancer was 92.3%.

https://ijeecs.iaescore.com/index.php/IJEECS/article/download/27351/16581

Breast cancer recognition based on performance evaluation of machine learning algorithms

In recent years, the number of breast cancer patients has increased rapidly. To increase the survival rate, the early diagnosis of breast cancer is very important. Hence, a trustworthy diagnosis and detection process is required. Automatic detection processes will be very helpful for medical practitioners. There are numerous proposed methods for timely sensing of breast cancer This study has suggested a deep neural network with feature selection techniques to predict breast cancer. The appraisal of the suggested strategy is performed on different evaluation benchmarks like train accuracy, test accuracy, precision, recall, specificity, sensitivity, f measure and MCC. Simulation result of the proposed method is very promising (accuracy 99.42%). Based on the findings of experimental simulations and analysis of the statistical data, the suggested method is efficient, beneficial and more accurate to predict breast cancer. As stated by the presented literature review, It is like setting an example among the prevailing machine learning strategies.

Breast Cancer Detection using Deep Neural Network

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Md. Habibur Rahaman

Author Tools

Chat about Author

Papers

Breast Cancer Detection using Deep Neural Network