Figure 1:
![(a) Normal cells; and (b) leukemia cells [9].](https://sciendo-parsed.s3.eu-central-1.amazonaws.com/65ccbc5b3bc2d770e76b839c/j_ijssis-2024-0013_fig_001.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=ASIA6AP2G7AKEPIMAVFE%2F20260317%2Feu-central-1%2Fs3%2Faws4_request&X-Amz-Date=20260317T010729Z&X-Amz-Expires=3600&X-Amz-Security-Token=IQoJb3JpZ2luX2VjEBUaDGV1LWNlbnRyYWwtMSJGMEQCICWDqwuWqBpJVbDRZevrg%2B65p9LKjDa%2FMTO59RVWRrCxAiAjf4wucpSoDvOxhOTuR8WxAiAQolsjiXIbTW%2F5XUa41SrFBQjf%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F8BEAIaDDk2MzEzNDI4OTk0MCIMQ1PV8v2JikvagPh9KpkFTQAwhxuD61QCRt%2Fc%2B8RL%2BR%2B8RU1XdWli%2FrBl8WtzHcxvH%2FXWJvdO2TpkTBgOorRmvY7cCn3Kf1vb6aIYuF%2B6nbyxlZ0MSte%2BogxKGHjCH1t4OYmVAEuXVHRqtIZLZVfIkBoFmDZyGJPx9nCng1s6%2BPeeSYoQV7nXKDOYetVTzqleES%2BMDUGOzv2VEyHH2ePMAbp2TqwbmK5YBTGVlyThsX7W8mbSD3A1hkGtwhrTf7Dse%2FgFQuOilHij%2BVEahxz9CSvCZdEzTIQlJ7JUqsJl9ZVRcrBAV8B2Bw7OivSkpO4pYTkdQtkqSKit5HaXHOLySxuRuPmgTEaWl5uBOuI08y2aHXFzmgRgd6yF1nCrzMCfU2MurZ4FBoSmSk6GKRz1pCDu%2BCLj%2BT1Rw0mBt3e11%2F9I2JABUKBzA2anYh1H3C2RjWnr8wkJH7InEZX%2FuIlf%2FLwVscZYJGQrhcxR0XhIs5P1fhC1SS3fgGN4dYolygowro2cjA8RmrWyasaP%2BwkAi2eJMHohl9jk8tcJX2E1CZ1ckUH59ZNg5CUCWFPDAbakJVMm73qGlz4tnzVirKDSqdbRMJuGPZwF5xK7zrYCGJ9PP%2FoaQJCpy%2Bpc%2FjQ7qCzNJnESjsavR4aQGkUuyjiazE43GP5z9%2BzUNwZeD3%2Fmks2rPZDE7V9K84i5pIszgXjtN5R0uoCIAl0DOspl0wImsmhUYHbE0aQcoHR4GO2GB7utCKqj8iSujqKYk5f5eVSg5oXF51h5T8zjIXP1W6z41Q46pM6pgbXH2yiI9RzUpT4GUAPBVAOXSbca9CL0J%2BYpXDDx3Fr2cwNcdyMiS5gw9hkiaDHlsZd20CjCbnaJrgi8bbwmIVWwMjj%2Fnerug6kYkPm3%2B58vZoAw%2BujhzQY6sgFi3JI0VKq%2FbGPcUY2tEqdjCKLH0f9%2BfC%2Bxfym38U3VT0Mu2%2BFWXYpnqYLZphfPe6tQzgyh6gScEt4oNcR57N%2FulnsVWNqVAS3nAPK9EUh7c3CracTNp3S9YQ30cSp3nMtAfDq6TXW19Ac4v3dOfBhcTmX2SxehanlQnH4o5ePufBsaighRTtNcCpuF0PBHyiXtnOaJhPcxbrG6rAmGQrq8HwR2gwomuNct7TkmqwCyzKEc&X-Amz-Signature=71b98ff3bd4939f5f8d9f0331e504b0da021f0b4205eb00315c62df9d50479a1&X-Amz-SignedHeaders=host&x-amz-checksum-mode=ENABLED&x-id=GetObject)
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Metrics showing performance metric of binary and multi-class classification_
| DL classifier algorithm | Class/dataset | Maximum training accuracy (%) | Maximum validation accuracy (%) |
|---|---|---|---|
| Modified VGG-16 classifier | Binary (ALL-IDB) | 98.50 | 68.33 |
| Modified InceptionNet classifier | 98.50 | 78.33 | |
| Ensemble classifier | 94.50 | 83.33 | |
| Modified VGG-16 classifier | Multi-class (real-images) | 98.56 | 93.20 |
| Modified InceptionNet classifier | 99.76 | 97.87 | |
| Ensemble classifier | 100 | 100 |
Comparison of the proposed approach with popular SOTA_
| Advantage criteria | Ensemble (VGG-16 + inception) | Pre-trained VGG-16 | Pre-trained inception | Random forest | SVM | ResNet50 (deep learning) | EfficientNet (deep learning) |
|---|---|---|---|---|---|---|---|
| Diversity in features | Yes | No | Yes | Yes | No | Yes | Yes |
| Generalization performance | Good | Good | Good | Good | Good | Excellent | Excellent |
| Robustness to overfitting | High | High | High | High | Moderate | High | High |
| Ensemble averaging benefit | Yes | No | No | No | No | No | No |
| Feature learning capabilities | Rich | Deep hierarchical | Diverse | Moderate | Linear | Deep hierarchical | Diverse |
| State-of-the-art performance | Yes | No (dated architecture) | Yes (at the time) | No | No | Yes | Yes (as of the time of training) |