Diabetic Retinopathy Detection Using Machine Learning
Volume-4 | Issue-1
Monocular Depth Estimation using a Multi-grid Attention-based Model
Volume-4 | Issue-3
Speedy Image Crowd Counting by Light Weight Convolutional Neural Network
Volume-3 | Issue-3
Construction of Efficient Smart Voting Machine with Liveness Detection Module
Volume-3 | Issue-3
An Economical Robotic Arm for Playing Chess Using Visual Servoing
Volume-2 | Issue-3
Triplet loss for Chromosome Classification
Volume-4 | Issue-1
Unstructured Noise Removal for Industrial Sensor Imaging Unit by Hybrid Adaptive Median Algorithm
Volume-3 | Issue-4
Real Time Sign Language Recognition and Speech Generation
Volume-2 | Issue-2
Analysis of Artificial Intelligence based Image Classification Techniques
Volume-2 | Issue-1
Design of ANN Based Machine Learning Method for Crop Prediction
Volume-3 | Issue-3
A REVIEW ON IOT BASED MEDICAL IMAGING TECHNOLOGY FOR HEALTHCARE APPLICATIONS
Volume-1 | Issue-1
COMPUTER VISION BASED TRAFFIC SIGN SENSING FOR SMART TRANSPORT
Volume-1 | Issue-1
Diabetic Retinopathy Detection Using Machine Learning
Volume-4 | Issue-1
Accurate Segmentation for Low Resolution Satellite images by Discriminative Generative Adversarial Network for Identifying Agriculture Fields
Volume-3 | Issue-4
Deep Learning based Handwriting Recognition with Adversarial Feature Deformation and Regularization
Volume-3 | Issue-4
State of Art Survey on Plant Leaf Disease Detection
Volume-4 | Issue-2
Optimal Compression of Remote Sensing Images Using Deep Learning during Transmission of Data
Volume-3 | Issue-4
OverFeat Network Algorithm for Fabric Defect Detection in Textile Industry
Volume-3 | Issue-4
VIRTUAL RESTORATION OF DAMAGED ARCHEOLOGICAL ARTIFACTS OBTAINED FROM EXPEDITIONS USING 3D VISUALIZATION
Volume-1 | Issue-2
Two-Stage Frame Extraction in Video Analysis for Accurate Prediction of Object Tracking by Improved Deep Learning
Volume-3 | Issue-4
Volume - 4 | Issue - 1 | march 2022
Published
28 February, 2022
The analysis of chromosomes, known as karyotyping, is essential in diagnosing various human genetic disorders and chromosomal aberrations. It can detect a variety of genetic diseases and provide a deeper insight into the human body. However, the process of manual karyotyping is highly time-consuming and requires accomplished professionals with a deep understanding in the field. An automated process is thus highly desirable to assist cytogeneticists and mitigate the cognitive load procured during karyotyping. With that intention, a similarity learning approach is proposed in this paper using ‘Triplet Loss’ for procuring high-dimensional embeddings. The Offline Triplet Loss, Semi-Hard Online mining, and associated hyperparameters are thoroughly tested and explored, and the obtained embeddings are used to classify the images into their respective chromosome classes and Denver groups. A comparative analysis on various embedding-classifying algorithms such as Multi-Layer Perceptron (MLP) and Nearest Neighbours is also demonstrated in this paper, along with experiments on associated distance metrics. The proposed methodologies deliver a superlative performance when compared to a baseline Convolutional Neural Network (CNN), on a publicly available chromosome classification dataset.
KeywordsDeep Learning Triplet Loss Karyotyping Siamese Network Image Classification
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