Lungs Disease Dataset (4 types)

Any damage that affects the normal functioning of the lungs is termed as a lung disease,

which can prove fatal if not detected early. To address this challenge, two innovative techniques proposed

for the lung disease classification, supporting medical professionals to diagnose and provides preventive

measures at an early stage. The proposed Model 1 integrates a custom MobileNetV2L2 architecture, that

builds upon the MobileNetV2 framework through fine-tuning and customization. This model incorporates a

ridge regularizer within its dense layer to enhance its performance. The Proposed Model 2, built on CNN as

its foundational block, is fine-tuned with ELU as the activation function, replacing ReLU, and incorporates

the L2 regularization technique. The proposed research utilizes two publicly available datasets: DS1(Data

Set1), which is the Lung Disease 5-class dataset, and DS2(Data Set2), which is the Lung Disease 4-class

dataset and are collected from Kaggle. The results from the proposed Model 1 provides better performance

than state-of-the-art techniques like EfficientNet B0, InceptionV3, ResNet, and InceptionResNetV2. It

achieved a training accuracy of 99.53%, validation accuracy of 100%, and test accuracy of 95.51%. The

proposed Model 2 provides outsatnding performance, with a training accuracy of 96.79%, validation

accuracy of 91.56%, and testing accuracy reaching 99.26% The proposed research serves as a valuable

tool for doctors, providing a secondary opinion in the diagnostic process.