Decision-support system for live detection of Leishmania parasites from microscopic images with Deep Learning

1. Overview This dataset consists of microscopic images of Giemsa-stained skin smears obtained from patients diagnosed with cutaneous leishmaniasis (CL). It is organized into two main parts: Dataset 1 → Collected with a Keyence BZ9000E digital microscope (lab-based). Dataset 2 → Extended dataset including all images from Dataset 1, plus an additional set collected with a Bresser Erudit DLX microscope (portable, low-cost). Both datasets contain paired Images (.png) and Labels (.txt), split into train, val, and test subsets. 2. Data Acquisition Dataset 1 Patients: 244 Libyan CL patients (confirmed by PCR at Tripoli University Hospital) Samples: Skin lesion smears (slit-skin or touch smears) Preparation: Air-dried, methanol-fixed, Giemsa-stained slides Imaging Setup: Microscope: Keyence BZ9000E (lab-grade) Magnification: 100× oil immersion objective Numerical Aperture (NA): 1.3 Resolution: 0.21 μm Image Count: 350 positive images (parasite densities: 1–100 amastigotes per image) 220 negative images (no parasites, controls) Total: 570 images Dataset 2 Patients: Additional cohort (6 patients) Samples: Same smear preparation method Imaging Setup: Microscope: Bresser Erudit DLX (portable, battery-powered) Camera: BRESSER MikroCam SP 5.0 Magnification: 100× oil immersion objective Numerical Aperture (NA): 1.25 Resolution: 0.22 μm Image Count: 106 positive images 58 negative images Total: 164 images 👉 Dataset 2 folder = Dataset 1 images + Dataset 2 images (extended dataset). 3. Directory Structure Dataset_1/ │ ├── Images/ │ ├── train/ # dataset_1_image_1.png ... dataset_1_image_398.png │ ├── val/ # continues numbering from train │ └── test/ │ └── Labels/ ├── train/ # dataset_1_image_1.txt ... ├── val/ └── test/ Dataset_2/ │ ├── Images/ │ ├── train/ # contains both dataset_1 and dataset_2 images │ ├── val/ │ └── test/ │ └── Labels/ ├── train/ ├── val/ └── test/ Naming Convention: dataset_1_image_X.png for Dataset 1 images dataset_2_image_X.png for Dataset 2 additional images Labels follow the same numbering with .txt extension Splits: Train, validation, and test sets are sequential Example: Train = images 1–398, Val = 399–…, Test = continues onward 4. Labels & Schema Image format: .png Label format: .txt (YOLO-style bounding boxes) Each line = one object (parasite body) Format: class_id: 0 = parasite Coordinates are normalized by image width and height. After class_id, the values are given in pairs: (x1 y1 x2 y2 … x4 y4) Every .png has a corresponding .txt file in the same split (train/, val/, test/). 5. Dataset Relations Dataset 1 = base dataset (lab microscope, high-quality). Dataset 2 = superset (Dataset_1 + portable microscope data). Train/Val/Test subsets are disjoint (sequential indexing prevents data leakage).