The document discusses using machine learning to detect malaria through analyzing images of red blood cells (RBCs). It notes that doctors currently diagnose malaria based on symptoms and medicine, but detection through images could help with consultation. The proposed approach would use image processing on a data set of RBC images to identify parasitized cells and generate an automated report on the parasite count and affected areas to assist diagnosis. The future scope includes developing an automated online doctor using this technique for proper diagnosis and treatment recommendations.

1. MALARIA DETECTION THROUGH MACHINE
LEARNING

2. DATA SET

3. WHY ?
• THE DOCTOR GIVES US MEDICINE IS BASED ON THE ANTIBIOTIC AND
ANTI-INFECTION TABLET.
• APART FROM ALL THE MEDICINE ONLY ONE MEDICINE IS FOR THE
SYMPTOMS.
• IF ANY DETECTION IS THERE IN THE IMAGE OF THE RBC WE CAN
CONSULT WITH THE DOCTOR.
• IN FUTURE WE AN UPDATE TO THE AUTOMATION AND PARTIULAR
MEDICINE.

4. WORKING(BASED ON IMAGE PROCESSING)

5. FUTURE SCOPE
• WE CAN ADD A AUTOMATED DOCTOR ON THE SERVER OR ON
WEBSITE SO IT WILL CURE PROPERLY.
• DEPENDING UPON THE MALARIA CELL (PARASITIZED COUNT) IT WILL
IDENTIFIDED THE AREA AND TRAINED DATA SET THE MACHINE WILL
GENERATED THE REPORT.