METAstasis in LYmph Nodes in oral squamous cell Carcinoma - Histopathology image Dataset AKA METALYNC-HD

METAstasis in LYmph Nodes in oral squamous cell Carcinoma - Histopathology image Dataset, also known as 'METALYNC-HD', was prepared for supporting the development and evaluation of Linux-based AI-driven methodologies for detecting metastatic involvement in lymph nodes. This dataset includes the following subsets: 'Training and Validation dataset' and 'Test dataset', which comprise high-resolution microscopic images and associated diagnostic labels (metastatic or metastasis-free). The 'Training and Validation dataset' contains 16,426 'Metastatic' image patches and 14,192 'Normal' image patches with a size of 256 x 256 pixels, which have been used to train the following five pre-trained deep neural network-based ImageNet models using the transfer learning approach: (i) InceptionV3, (ii) MobileNetV2, (iii) Xception, (iv) ResNet50, and (v) InceptionResNetV2. The Xception model showed the best fit and classification performance. Using the fine-tuned Xception model as a backbone, Automated Lymph Node Metastasis Detection Software (ALNMDS) was developed. ALNMDS was tested using the Evaluation dataset that contains 2533 histological images (1024 x 768 pixels) of lymph node metastasis and 999 metastasis-free images (1024 x 768 pixels) of lymph nodes. No patient data has been shared in this dataset. The record is publicly accessible upon request and consideration of the competent authority at the Department of Oral Pathology, Maulana Azad Institute of Dental Sciences, New Delhi, India. The files can only be accessed by following the approval of the project supervisor.