Python
import pandas as pd
import matplotlib.pyplot as plt

# Dataset performance data
data = {
    "Dataset": ["IDRiD", "Kaggle APTOS", "MESSIDOR", "EyePACS", "DIARETDB1", "e-ophtha"],
    "Image Resolution": ["4288 x 2848", "1024 x 1024", "2240 x 1488", "640 x 480", "1500 x 1152", "1440 x 960"],
    "Model Used": ["FR-UNET", "FR-UNET", "DeepLabV3+", "U-Net", "ResUNet", "Attention U-Net"],
    "Accuracy (%)": [96.5, 95.8, 94.2, 93.1, 92.0, 91.5],
    "Sensitivity (%)": [95.3, 94.6, 92.7, 91.5, 89.6, 90.1],
    "Specificity (%)": [97.1, 96.3, 95.0, 94.2, 93.3, 92.8],
    "F1-Score": [0.94, 0.93, 0.91, 0.90, 0.88, 0.87],
    "IoU (%)": [89.2, 87.8, 86.5, 85.3, 82.7, 81.9]
}

# Create a DataFrame
df = pd.DataFrame(data)

# Display the table
print(df)

# Plot the comparison as a bar chart for Accuracy, IoU
fig, ax = plt.subplots(figsize=(10, 6))

# Plot for Accuracy
df.plot(x="Dataset", y="Accuracy (%)", kind="bar", ax=ax, color="blue", label="Accuracy", position=0)

# Plot for IoU
df.plot(x="Dataset", y="IoU (%)", kind="bar", ax=ax, color="green", label="IoU", position=1)

# Add labels and title
ax.set_ylabel('Percentage (%)')
ax.set_title('Dataset Performance Comparison')
ax.legend(["Accuracy", "IoU"])

# Show plot
plt.xticks(rotation=45, ha="right")
plt.tight_layout()
plt.show()
Dataset Image Resolution       Model Used  Accuracy (%)  \
0         IDRiD      4288 x 2848          FR-UNET          96.5   
1  Kaggle APTOS      1024 x 1024          FR-UNET          95.8   
2      MESSIDOR      2240 x 1488       DeepLabV3+          94.2   
3       EyePACS        640 x 480            U-Net          93.1   
4     DIARETDB1      1500 x 1152          ResUNet          92.0   
5      e-ophtha       1440 x 960  Attention U-Net          91.5   

   Sensitivity (%)  Specificity (%)  F1-Score  IoU (%)  
0             95.3             97.1      0.94     89.2  
1             94.6             96.3      0.93     87.8  
2             92.7             95.0      0.91     86.5  
3             91.5             94.2      0.90     85.3  
4             89.6             93.3      0.88     82.7  
5             90.1             92.8      0.87     81.9
Python
import pandas as pd
import matplotlib.pyplot as plt

# Dataset performance data
data = {
    "Dataset": ["IDRiD", "Kaggle APTOS", "MESSIDOR", "EyePACS", "DIARETDB1", "e-ophtha"],
    "Image Resolution": ["4288 x 2848", "1024 x 1024", "2240 x 1488", "640 x 480", "1500 x 1152", "1440 x 960"],
    "Model Used": ["FR-UNET", "FR-UNET", "DeepLabV3+", "U-Net", "ResUNet", "Attention U-Net"],
    "Accuracy (%)": [96.5, 95.8, 94.2, 93.1, 92.0, 91.5],
    "Sensitivity (%)": [95.3, 94.6, 92.7, 91.5, 89.6, 90.1],
    "Specificity (%)": [97.1, 96.3, 95.0, 94.2, 93.3, 92.8],
    "F1-Score": [0.94, 0.93, 0.91, 0.90, 0.88, 0.87],
    "IoU (%)": [89.2, 87.8, 86.5, 85.3, 82.7, 81.9]
}

# Create a DataFrame
df = pd.DataFrame(data)

# Display the table
print(df)

# Plot the comparison as a bar chart for Accuracy, IoU
fig, ax = plt.subplots(figsize=(10, 6))

# Plot for Accuracy
df.plot(x="Dataset", y="Accuracy (%)", kind="bar", ax=ax, color="blue", label="Accuracy", position=0)

# Plot for IoU
df.plot(x="Dataset", y="IoU (%)", kind="bar", ax=ax, color="green", label="IoU", position=1)

# Add labels and title
ax.set_ylabel('Percentage (%)')
ax.set_title('Dataset Performance Comparison')
ax.legend(["Accuracy", "IoU"])

# Show plot
plt.xticks(rotation=45, ha="right")
plt.tight_layout()
plt.show()
Dataset Image Resolution       Model Used  Accuracy (%)  \
0         IDRiD      4288 x 2848          FR-UNET          96.5   
1  Kaggle APTOS      1024 x 1024          FR-UNET          95.8   
2      MESSIDOR      2240 x 1488       DeepLabV3+          94.2   
3       EyePACS        640 x 480            U-Net          93.1   
4     DIARETDB1      1500 x 1152          ResUNet          92.0   
5      e-ophtha       1440 x 960  Attention U-Net          91.5   

   Sensitivity (%)  Specificity (%)  F1-Score  IoU (%)  
0             95.3             97.1      0.94     89.2  
1             94.6             96.3      0.93     87.8  
2             92.7             95.0      0.91     86.5  
3             91.5             94.2      0.90     85.3  
4             89.6             93.3      0.88     82.7  
5             90.1             92.8      0.87     81.9