Detection and Classification of Zebrafish Embryonic
Phenotypes to Signaling Pathways
Paper ID: ICPCSN-1088
Authors
Sri Vyshnav Veeravalli
Rayala Manogna
Co - Author
Dr. A. Robert Singh
ABSTRACT
The ability to accurately detect and classify zebrafish embryonic phenotypes is crucial for
understanding developmental biology and evaluating the effects of genetic and chemical
perturbations on signaling pathways. This study presents an advanced deep learningbased framework using YOLOv8 for automated identification and categorization of the
anatomical features and developmental phases of zebrafish. A dataset is used to train the
model. Comprising 17 distinct phenotype categories, including Abdomen, Blastomere,
Dead, Epiboly, Eye, Head, Somite, Tail, Yolk, and multiple developmental stages
(Stage1–Stage7, Tail Extension, etc.). The YOLOv8 architecture is fine-tuned for highprecision phenotypic identification, leveraging its real-time object detection capabilities
and improved feature extraction mechanisms.
PROBLEM STATEMENT
The process of identifying and classifying zebrafish embryo phenotypes has historically
depended on manual observation, which introduces several limitations. This approach is not
only labor-intensive and slow but also prone to human error, subjective interpretations, and
lacks scalability. Due to these constraints, researchers struggle to perform large-scale
phenotypic analyses efficiently, especially when associating developmental stages with
specific signaling pathways. The primary objective of this project is to develop a fully
automated system capable of detecting and classifying zebrafish embryonic phenotypes
efficiently and accurately. By utilizing a hybrid machine learning model that combines
YOLO for the detection of embryos in images and ResNet50 for classification into different
developmental stages, the system aims to provide a more reliable and standardized method
for phenotype classification.
Literature Survey
ARCHITECTURE DIAGRAM
MODULES
Data Processing Module:
Prepares images through resizing, normalization, and noise reduction.
YOLO detects embryos, ResNet50 classifies developmental stages.
Data Storage Module:
Stores raw and processed images, model results, and metrics.
Enables easy retrieval and scalability for high-throughput analysis.
Hybrid Model Architecture:
YOLO detects and crops embryo regions.
ResNet50 classifies cropped embryos into Day1-Day7.
Stage mapped to corresponding signaling pathways.
Outputs embryo location, stage, and pathway.
Model Training
Training Setup
Optimizers Helps the model converge faster by minimizing the loss function during
backpropagation.
YOLO Training Loss: Combines bounding box regression loss, objectness loss, and
classification loss.
ResNet50 Training Loss: Uses Cross-Entropy Loss to classify embryo stages.
The loss function guides the model in learning to make correct predictions.
Validation Process
Monitored metrics include Accuracy, Precision, Recall, F1 Score, and mAP (for YOLO).
These metrics evaluate the performance of the model on unseen data.
A separate validation set is used that is not part of the training set.
Results are used to adjust hyperparameters or early stopping to prevent overfitting.
Validation ensures the model generalizes well to new, unseen data.
Model Reference
Image Preprocessing:
The raw zebrafish embryo images undergo preprocessing steps to prepare them
for inference.
Noise reduction to remove unwanted disturbances. Resizing and normalization
to standardize images for YOLO and ResNet50 input requirements.
This ensures that the model receives clean, uniform, and optimized data,
leading to higher accuracy during inference.
Inference Function:
The YOLO model detects embryos within the preprocessed images by identifying
and localizing them through bounding boxes.
The detected embryo regions are cropped and passed to the ResNet50 model for
stage classification.
ResNet50 performs inference on each embryo, predicting its developmental stage
(Day1 to Day7).
Model Reference
Path Processing:
After classification, the predicted embryo stage is mapped to the respective signaling
pathways (e.g., BMP, RA, Wnt, FGF, Nodal, Shh, PCP).
The pathway processing module utilizes predefined biological mappings to associate
stages with pathways.
This module ensures that each classified embryo is tagged not only with its stage but
also with its probable affected signaling pathway, adding biological interpretability
to the model’s output.
The final output is structured data containing:
Embryo location
Developmental stage
Mapped signaling pathway
Original Data
RESULTS
OUTPUT
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