DEEPLUNGNET: ACCURATE LUNG CANCER PREDICTION USING INCEPTION V3-BASED CNN ON HISTOPATHOLOGICAL IMAGES

Registration ID: IJNRD_227176

Published ID: IJNRD2408375

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Syed Arfath Ali, Dr. Abdul Khadeer

Lung cancer is still a major global health concern that requires early and accurate detection in order to effectively cure. This effort uses CNNs (convolutional neural networks) to predict lung cancer using a un ique technique. Specifically, the Inception V3 armature is used. The project's goal is to create a robust, accurate model for classifying lung cancer. The dataset utilized for the experiment includes histological pictures of the lungs, which are divided into three groups: lung scaled cell melanoma, lung adenocarcinoma, and lung benign tissue. Python is used to enforce the design, handling the intricacies of deep literacy jobs with its erratic libraries and fabrics. The training and confirmation dataset consists of 15,000 histopathological photos that were artificially generated from 750 original lung towel images. To further enhance its quality and ethical compliance, the dataset is validated and biddable under HIPAA. Achieving high model delicacy requires careful consideration at every stage of the design process. The model successfully absorbed the complexities of the training data, as evidenced by the emotional training delicacy of 94.00 that was obtained during the training phase. Additionally, the confirmation delicacy was 93.000, demonstrating the model's capacity to generate predictions from unobserved data. The model's ability to capture intricate patterns and features found in lung tissue images is made possible by the InceptionV3 armature, which allows for accurate bracketing across the three different classes. Transfer literacy allows the model to take advantage of InceptionV3's pre-trained weights on large-scale picture datasets, which speeds up training and improves the model's capacity to identify characteristics relevant to the lung cancer bracket. This design could make it possible for doctors to identify lung cancer at an earlier stage, which might result in quicker treatment and improved patient outcomes. Because of the model's high level of conceptualization and delicacy, medical personnel may use it implicitly in real-world circumstances to aid in the fight against lung cancer. It is a significant addition to the fields of medical image analysis and lung cancer diagnosis due to its high accuracy and ability to handle a dataset with three classifications. The effort creates the foundation for further investigation and discourse on CNN-based models in other medical imaging procedures, which might significantly alter the way we identify and manage illnesses of color.

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Syed Arfath Ali & Dr. Abdul Khadeer (August-2024). DEEPLUNGNET: ACCURATE LUNG CANCER PREDICTION USING INCEPTION V3-BASED CNN ON HISTOPATHOLOGICAL IMAGES. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(8), d357-d365. https://ijnrd.org/papers/IJNRD2408375.pdf

Volume 9 Issue 8, August-2024

Pages : d357-d365

Paper Reg. ID: IJNRD_227176

Published Paper Id: IJNRD2408375

Research Area: Engineering

Author Type: Indian Author

Country: -, -, India

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