research / blood-cell-detection · Graduate coursework (CSE583, Digital Image Processing), North South University · 2023
Deep Learning-Based Blood Cell Detection in Microscopic Images for Enhanced Disease Recognition with RetinaNet
Md. Abu Ammar, Sadia Afrin Tamanna
[computer-vision][object-detection][retinanet][medical-imaging][transfer-learning]
Abstract
Fine-tuning a pretrained RetinaNet (ResNet backbone + feature pyramid network, focal loss) on the BCCD microscopy dataset to detect red blood cells, white blood cells, and platelets — 364 images, 4,888 annotations, three classes — reaching mAP 0.876 at IoU 0.5 and 55.25% at IoU 0.50:0.95 on the test split.
§1In plain words
Counting blood cells under a microscope is slow, tiring, expert work, and diagnosis depends on it. This project teaches an off-the-shelf object detector to draw boxes around every red cell, white cell, and platelet in a slide image. The most consequential decision was what not to do: no color augmentation during training, because a blood cell's color is diagnostic information — blur or hue-shift it and you've destroyed the very signal a hematologist reads.
§2Method
Torchvision RetinaNet with a modified classification head for 3 classes; transfer learning from pretrained weights. BCCD (Pascal VOC format), 255/73/36 train/val/test, resized to 640×640 with bounding-box sanity-clipping; augmentation restricted to flips and rotations. Trained 40 epochs, batch size 4, lr 0.001, on a 4 GB RTX 3050 Ti — a deliberately modest hardware budget.
§3Results
mAP 0.876 @ IoU 0.5 and 55.25% @ 0.50:0.95 on test. Honest comparison: specialized YOLO variants do better on BCCD (YOLOv5x 0.923, CST-YOLO 0.927 @ 0.5) — the fine-tuned single-stage RetinaNet gets close without architecture surgery, which was the point of the exercise.
§4Looking back
This was my proving ground for domain-aware augmentation policy — the same "know which invariances are real" reasoning I now apply when validating vision models for kiosk monitoring at work. The write-up itself has a contradictory sentence left from an early draft, so the PDF is available on request rather than posted.