A content-based image retrieval system for the diagnosis of lymphoma using blood micrographs: An incorporation of deep learning with a traditional learning approach.

Lymphomas, or cancers of the lymphatic system, account for around half of all blood cancers diagnosed each year. Lymphoma is a condition that is difficult to diagnose, and accurate diagnosis is critical for effective treatment. Manual microscopic analysis of blood cells requires the involvement of medical experts, whose precision is dependent on their abilities, and it takes time.

Localization and recognition of leukocytes in peripheral blood: A deep learning approach.

Automatic recognition and classification of leukocytes helps medical practitioners to diagnose various blood-related diseases by analysing their percentages. Different researchers have come up with different algorithms that use traditional learning for the classification of different types of leukocytes. In contrast to traditional learning, in which no knowledge is retained that can be transferred from one model to another, our proposed algorithm uses deep learning approach for segmentation and classification.

Retrieval of brain MRI with tumor using contrastive loss based similarity on GoogLeNet encodings.

An image retrieval system for medical images aids in disease diagnosis by providing similar images from the medical database to a query image. In this article, a content-based medical image retrieval (CBMIR) system is proposed for the retrieval of magnetic resonance imaging (MRI) images of the brain with three types of tumors:- meningioma, glioma and pituitary tumors. The proposed system uses GoogLeNet encodings via transfer learning as image features.

Brain tumor classification using deep CNN features via transfer learning.

Brain tumor classification is an important problem in computer-aided diagnosis (CAD) for medical applications. This paper focuses on a 3-class classification problem to differentiate among glioma, meningioma and pituitary tumors, which form three prominent types of brain tumor. The proposed classification system adopts the concept of deep transfer learning and uses a pre-trained GoogLeNet to extract features from brain MRI images.

An enhanced deterministic K-Means clustering algorithm for cancer subtype prediction from gene expression data.

Background: Clustering algorithms with steps involving randomness usually give different results on different executions for the same dataset. This non-deterministic nature of algorithms such as the K-Means clustering algorithm limits their applicability in areas such as cancer subtype prediction using gene expression data. It is hard to sensibly compare the results of such algorithms with those of other algorithms.