Advancing Medical Image Classification: Beyond Grid-Based Analysis with Hybrid CNN-LSTM Deep Learning Techniques

Abstract

Medical imageaclassification is a critical area ofafocus in theafield of healthcare, asait plays aavital role in automating diseaseidentification. Deepconvolutionalaneuralnetworks(CNNs) and traditional machine learning techniques have shown promise in accurately classifying medicalaimages. However, these methods often struggle toacapture the long-term and hier archical relationships among visual features white preserving theaspatial and structural in formation inherent in the images. To address this challenge, researchers have developeda in novative approaches to enhance the performance of medical image classification. In this pa per, we present two such methods that contributeato the advancement of this field. The first method focuses on transformingagrid-based data intoahigher-dimensionalarepresentations usingaunstructured point cloud data structures. Conventionally, medical image classifica tion techniquesarely on data with a consistent grid structure, limiting their overall perfor mance. By leveraging point cloud representations, we overcome thisalimitation and enable the extraction of intricate details from the original images. This novel approach significantly improves classification accuracy, as demonstrated in ouraexperiments with a publicly ac cessible brain tumor dataset. The second method involves a hybrid solution that effectively models the relationship between visualaimage features and label information inamedicalim age data. By combining CNNsandLongShort-Term Memory(LSTM)networks, weharness the strengthsaof both architectures. The CNN learns the structured properties of the images, white the LSTM capturesathe dependencies in the spectral domain.aThis hybrid model out performs existing state-of-the-art models, achieving remarkable accuracy rates ofa93% for skin cancer data and 96% for brain tumor data. Ouracomparison with otherarenowned models further validates the superiority of this approach. Both methods contribute to the improvement of medical image classification by addressing thealimitations of conventional techniques. By transforming grid-based data into higher-dimensionalarepresentations and leveraging the power of hybrid CNN-LSTMamodels, we enhance accuracy, enable the ex traction of moreadetailed information,aand facilitate the automation of disease identifica tion. These advancements have significant implications for medical professionals, enabling themato diagnose diseases more accurately and efficiently,aultimately leading to improved patient outcomes.