Enhanced Early Detection of Thyroid Abnormalities using a Hybrid Deep Learning Model: A Sequential CNN and K-Means Clustering Approach

The thyroid gland, often referred to as the butterfly gland due to its shape, is located in the neck and plays a crucial role in regulating metabolism. It is susceptible to various health conditions, including hypothyroidism, hyperthyroidism, thyroid cancer, and thyroid nodules. Early detection of these conditions is essential for accurate diagnosis and effective treatment. Detecting thyroid nodules using machine learning and deep learning techniques presents a challenging yet promising research avenue. The choice of model depends on the characteristics of the patient's thyroid data, the dataset size, and the available computational resources. Hybrid models can be employed to handle complex data more effectively. In this study, a sequential Convolutional Neural Network (CNN) model was developed due to its capability to automate feature extraction and focus on Regions-of-Interest (ROIs) for detecting thyroid abnormalities. The proposed model achieved an accuracy of 81.5%, with a precision of 97.4% and a sensitivity of 83.1%, indicating its robustness in classifying images as benign or malignant. The confusion matrix provided further performance insights. Data segmentation was enhanced using K-means clustering for its scalability and efficiency in processing large medical image datasets. Compared to traditional models, the proposed hybrid approach demonstrated a significant improvement in diagnostic accuracy and precision, achieving performance gains of approximately 15-20% over baseline methods. These advancements underscore the potential of integrating machine learning and deep learning in medical diagnostics, paving the way for more reliable and efficient diagnostic tools for healthcare professionals

Thyroid Diagnosis from SPECT Images Using Convolutional Neural Network with Optimization

Thyroid disease has now become the second largest disease in the endocrine field; SPECT imaging is particularly important for the clinical diagnosis of thyroid diseases. However, there is little research on the application of SPECT images in the computer-aided diagnosis of thyroid diseases based on machine learning methods. A convolutional neural network with optimization-based computer-aided diagnosis of thyroid diseases using SPECT images is developed. Three categories of diseases are considered, and they are Graves’ disease, Hashimoto disease, and subacute thyroiditis. A modified DenseNet architecture of convolutional neural network is employed, and the training method is improved. The architecture is modified by adding the trainable weight parameters to each skip connection in DenseNet. And the training method is improved by optimizing the learning rate with flower pollination algorithm for network training. Experimental results demonstrate that the proposed method of convolutional neural network is efficient for the diagnosis of thyroid diseases with SPECT images, and it has superior performance than other CNN methods

Radiomics in [¹⁸F]FDG PET/CT:A leap in the dark?

Positron emission tomography (PET) imaging with the non-metabolisable glucose analogue 2-[18F]fluoro-2-deoxy-D-glucose ([18F]FDG), combined with low dose computed tomography (CT) for anatomical reference, is an important tool to detect and stage cancer or active inflammations. Visual interpretation of PET/CT images consists of (qualitative) assessment of radiotracer uptake in different tissues and their density. Furthermore, the location, size, shape, and relation with surrounding tissues of these lesions provide important clues on their nature. Yet, medical images contain much more information about tissue biology hidden in the myriad of voxels of both lesions and healthy tissue than can be assessed visually. Quantification of radiotracer uptake heterogeneity and other tissue characteristics is studied in the field of radiomics. Radiomics is a form of medical image processing that aims to find stable and clinically relevant image-derived biomarkers for lesion characterisation, prognostic stratification, and response prediction, thereby contributing to precision medicine. Radiomics consists of the conversion of (parts of) medical images into a high-dimensional set of quantitative features and the subsequent mining of this dataset for potential information useful for the quantification or monitoring of tumour or disease characteristics in clinical practice. This thesis contributed to a deeper understanding of the methodological aspects of handcrafted radiomics in [18F]FDG PET/CT, specifically in small datasets. However, most radiomic papers present proof-of-concept studies and clinical implementation is still far away. At some point in the future, radiomic biomarkers may be used in clinical practice, but at the moment we should acknowledge the limitations of the field and try to overcome these. Only then, we will be able to cross the translational gap towards clinical readiness. Future research should focus on standardisation of feature selection, model building, and ideally a tool that implements these aspects. In such a way, radiomics may redeem the promise of bringing forth imaging biomarkers that contribute to precision medicine.<br/