Malaria Detection and Classificaiton

• URL: http://arxiv.org/abs/2011.14329v1
• Date: Sun, 29 Nov 2020 10:04:01 GMT
• Title: Malaria Detection and Classificaiton
• Authors: Ruskin Raj Manku and Ayush Sharma and Anand Panchbhai
• Abstract summary: Malaria is a disease of global concern according to the World Health Organization. In this work, we put forward a framework for diagnosis of Malaria. We adopt a two layer approach, where we detect infected cells using a Faster-RCNN in the first layer, crop them out, and feed the cropped cells to a seperate neural network for classification.
• Score: 0.38233569758620056
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Malaria is a disease of global concern according to the World Health Organization. Billions of people in the world are at risk of Malaria today. Microscopy is considered the gold standard for Malaria diagnosis. Microscopic assessment of blood samples requires the need of trained professionals who at times are not available in rural areas where Malaria is a problem. Full automation of Malaria diagnosis is a challenging task. In this work, we put forward a framework for diagnosis of malaria. We adopt a two layer approach, where we detect infected cells using a Faster-RCNN in the first layer, crop them out, and feed the cropped cells to a seperate neural network for classification. The proposed methodology was tested on an openly available dataset, this will serve as a baseline for the future methods as currently there is no common dataset on which results are reported for Malaria Diagnosis.

Related papers

• Assessing and Enhancing Large Language Models in Rare Disease Question-answering [64.32570472692187]
  We introduce a rare disease question-answering (ReDis-QA) dataset to evaluate the performance of Large Language Models (LLMs) in diagnosing rare diseases. We collected 1360 high-quality question-answer pairs within the ReDis-QA dataset, covering 205 rare diseases. We then benchmarked several open-source LLMs, revealing that diagnosing rare diseases remains a significant challenge for these models. Experiment results demonstrate that ReCOP can effectively improve the accuracy of LLMs on the ReDis-QA dataset by an average of 8%.
  arXiv  Detail & Related papers  (2024-08-15T21:09:09Z)
• Malaria Cell Detection Using Deep Neural Networks [1.1237179306040028]
  Malaria remains one of the most pressing public health concerns globally. Traditional diagnostic methods, such as microscopic examination of blood smears, are labor-intensive. This project aims to automate the detection of malaria-infected cells using a deep learning approach.
  arXiv  Detail & Related papers  (2024-06-28T15:44:55Z)
• CodaMal: Contrastive Domain Adaptation for Malaria Detection in Low-Cost Microscopes [51.5625352379093]
  Malaria is a major health issue worldwide, and its diagnosis requires scalable solutions that can work effectively with low-cost microscopes (LCM) Deep learning-based methods have shown success in computer-aided diagnosis from microscopic images. These methods need annotated images that show cells affected by malaria parasites and their life stages. Annotating images from LCM significantly increases the burden on medical experts compared to annotating images from high-cost microscopes (HCM)
  arXiv  Detail & Related papers  (2024-02-16T06:57:03Z)
• Simulating Malaria Detection in Laboratories using Deep Learning [0.0]
  Malaria is usually diagnosed by a microbiologist by examining a small sample of blood smear. Reducing mortality from malaria infection is possible if it is diagnosed early and followed with appropriate treatment. The WHO has set audacious goals of reducing malaria incidence and mortality rates by 90% in 2030 and eliminating malaria in 35 countries by that time.
  arXiv  Detail & Related papers  (2023-03-21T11:23:59Z)
• Auditing Algorithmic Fairness in Machine Learning for Health with Severity-Based LOGAN [70.76142503046782]
  We propose supplementing machine learning-based (ML) healthcare tools for bias with SLOGAN, an automatic tool for capturing local biases in a clinical prediction task. LOGAN adapts an existing tool, LOcal Group biAs detectioN, by contextualizing group bias detection in patient illness severity and past medical history. On average, SLOGAN identifies larger fairness disparities in over 75% of patient groups than LOGAN while maintaining clustering quality.
  arXiv  Detail & Related papers  (2022-11-16T08:04:12Z)
• Federated Learning Enables Big Data for Rare Cancer Boundary Detection [98.5549882883963]
  We present findings from the largest Federated ML study to-date, involving data from 71 healthcare institutions across 6 continents. We generate an automatic tumor boundary detector for the rare disease of glioblastoma. We demonstrate a 33% improvement over a publicly trained model to delineate the surgically targetable tumor, and 23% improvement over the tumor's entire extent.
  arXiv  Detail & Related papers  (2022-04-22T17:27:00Z)
• End-to-end Malaria Diagnosis and 3D Cell Rendering with Deep Learning [0.0]
  Malaria is a parasitic infection that poses a significant burden on global health. It kills one child every 30 seconds and over one million people annually. The current gold standard for diagnosing malaria requires microscopes, reagents, and other equipment that most patients of low socioeconomic brackets do not have access to.
  arXiv  Detail & Related papers  (2021-07-08T08:13:11Z)
• A Dataset and Benchmark for Malaria Life-Cycle Classification in Thin Blood Smear Images [7.113350536579545]
  Malaria microscopy, microscopic examination of stained blood slides to detect parasite Plasmodium, is considered to be a gold-standard for detecting malaria. We propose to create a deep learning-based method to automatically detect (localize) the plasmodium parasites in the photograph of stained film. To facilitate the research in machine learning-based malaria microscopy, we introduce a new large scale microscopic image malaria dataset.
  arXiv  Detail & Related papers  (2021-02-17T11:44:52Z)
• UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data [81.00385374948125]
  We present UNcertaInTy-based hEalth risk prediction (UNITE) model. UNITE provides accurate disease risk prediction and uncertainty estimation leveraging multi-sourced health data. We evaluate UNITE on real-world disease risk prediction tasks: nonalcoholic fatty liver disease (NASH) and Alzheimer's disease (AD) UNITE achieves up to 0.841 in F1 score for AD detection, up to 0.609 in PR-AUC for NASH detection, and outperforms various state-of-the-art baselines by up to $19%$ over the best baseline.
  arXiv  Detail & Related papers  (2020-10-22T02:28:11Z)
• MOSQUITO-NET: A deep learning based CADx system for malaria diagnosis along with model interpretation using GradCam and class activation maps [9.01199960262149]
  Malaria is one of the deadliest diseases in today world which causes thousands of deaths per year. The parasites responsible for malaria are scientifically known as Plasmodium which infects the red blood cells in human beings. The diagnosis of malaria requires identification and manual counting of parasitized cells by medical practitioners in microscopic blood smears.
  arXiv  Detail & Related papers  (2020-06-17T13:00:30Z)
• Cross-lingual Transfer Learning for COVID-19 Outbreak Alignment [90.12602012910465]
  We train on Italy's early COVID-19 outbreak through Twitter and transfer to several other countries. Our experiments show strong results with up to 0.85 Spearman correlation in cross-country predictions.
  arXiv  Detail & Related papers  (2020-06-05T02:04:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.