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This project focuses on the application of Convolutional Neural Networks (CNN) and Transfer Learning techniques to classify malaria from cell images. In this project, we develop a CNN model by using Transfer Learning to leverage knowledge from pre-trained networks with a dataset specifically compiled for malaria-infected and uninfected blood smear images. The objective is to accurately distinguish between infected and uninfected cells, thereby assisting in rapid and efficient malaria diagnosis.

app.py

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+import streamlit as st 
+from tensorflow.keras.models import load_model
+from PIL import Image
+import numpy as np
+
+model = load_model("model_malaria_detection.h5")
+
+def process_image(img):
+    img = img.convert("RGB")
+    img = img.resize((50,50))
+    img = np.array(img)
+    if img.ndim == 2:
+        img = np.stack((img,)*3, axis=-1)
+    img = img/255.0
+    img = np.expand_dims(img, axis=0)
+    return img
+
+st.title("MALARIA RECOGNITION")
+st.divider()
+
+col1, col2, col3 = st.columns([1,2,1])
+with col2:
+    st.image("malaria.jpeg")
+st.divider()
+
+st.success("Upload your malaria image from blood cell and classify the images with the following labels: Uninfected and Parasitized with CNN deep learning.")
+st.divider()
+
+st.write("Upload your image and see the results")
+st.divider()
+
+file = st.file_uploader("Choose an image", type=["jpg", "jpeg", "png", "webp"])
+
+if file is not None:
+    img = Image.open(file)
+    st.image(img, caption="Uploaded image")
+    image = process_image(img)
+    prediction = model.predict(image)
+    predicted_class = np.round(prediction)
+    predicted_class = int(predicted_class.flatten())
+    
+
+    
+    class_names = {0:"Parasitized", 1:"Uninfected"}
+    
+    st.write(f"Predicted Malaria Type: {class_names[predicted_class]}")

model_malaria_detection.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:35a2f3b6e2c895b7c2cc86ec5a2bc9028d1359f8ee3924a6f2597b51a309a1e1
+size 67088408

requirements.txt

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+streamlit
+tensorflow