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Related Experiment Video

Updated: Jun 9, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Automating cancer diagnosis using advanced deep learning techniques for multi-cancer image classification.

Yogesh Kumar¹, Supriya Shrivastav², Kinny Garg³

¹Department of Computer Science and Engineering, School of Technology, PDEU, Gandhinagar, Gujarat, 382426, India.

Scientific Reports

|October 24, 2024

Summary

This summary is machine-generated.

This study introduces AI-powered cancer detection using deep learning models. DenseNet121 achieved 99.94% accuracy, demonstrating AI

Keywords:

Cancer Detection Deep learning Medical imaging Multi-cancer diagnosis Noise removal Radiation Therapy

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Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Cancer is a leading cause of death globally, necessitating early detection.
Traditional methods are often invasive and time-consuming.
There is a need for efficient and accurate automated cancer detection solutions.

Purpose of the Study:

To evaluate deep learning models for automated cancer detection.
To compare the performance of various Convolutional Neural Networks (CNNs).
To identify the most effective AI model for multi-cancer image analysis.

Main Methods:

Utilized deep learning models including DenseNet121, DenseNet201, Xception, InceptionV3, MobileNetV2, NASNetLarge, NASNetMobile, InceptionResNetV2, VGG19, and ResNet152V2.
Applied image segmentation and contour feature extraction (perimeter, area, epsilon).
Evaluated models on image datasets for seven cancer types: brain, oral, breast, kidney, Acute Lymphocytic Leukemia, lung and colon, and cervical cancer.

Main Results:

DenseNet121 achieved the highest validation accuracy at 99.94% with a loss of 0.0017.
DenseNet121 demonstrated the lowest Root Mean Square Error (RMSE) for both training (0.036056) and validation (0.045826).
The study successfully identified DenseNet121 as the top-performing model.

Conclusions:

AI-based techniques, particularly deep learning, significantly enhance cancer detection accuracy.
DenseNet121 shows exceptional capability for automated detection across multiple cancer types.
This research highlights the potential of AI in improving early cancer diagnosis and patient outcomes.