Black Fungus Detection Using Machine Learning

Registration ID: IJNRD_196190

Published ID: IJNRD2305653

DOI: Click Here to Get

Sabappa , Aishwarya sajjan , Shilpa , Pooja , Sushma T Shedole

Machine Learning,CNN

Fungus is extremely disreputable and dangerous for human health and cause various life-threatening disease to humans. Thousands of different fungus species exist in the world and spores always present in environment. Its sign and symptom are non-specific. Mucor mycosis also known as black fungus is a fungal infection that causes discoloration over nose and eye, blurred or double vision, chest pain, breathing difficulties, fever and cough. The main aim of this project is to analyses and predict the infection probability based on black fungus images with the help of fungus detection system and algorithms to make automatically detects fungus using machine learning techniques. We are using in project CNN algorithm. The dataset used is raw data based on the pulmonary Mucor mycosis symptoms.

Published Paper   E-Certificate

Sabappa, Aishwarya sajjan, Shilpa , Pooja, & Sushma T Shedole (May-2023). Black Fungus Detection Using Machine Learning. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(5), g428-g431. https://ijnrd.org/papers/IJNRD2305653.pdf

Volume 8 Issue 5, May-2023

Pages : g428-g431

Paper Reg. ID: IJNRD_196190

Published Paper Id: IJNRD2305653

Downloads: 000121975

Research Area: Computer Engineering 

Country: Raichur, Karnataka, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2305653.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2305653

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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