A Novel Deep Learning Approach for Accurate Cancer Type and Subtype Identification

Abstract

Cancer is a disease where abnormal cells grow uncontrollably and spread to other body parts. It can originate anywhere in the human body, which consists of trillions of cells. These cells continually divide, replenishing the body’s needs. As cells age or sustain damage, they naturally undergo apoptosis, allowing new cells to take their place. Our research uses a secondary dataset from Kaggle, comprising over 130,000 images representing various cancer types. We have developed a novel Deep-learning model capable of detecting and classifying cancer at early stages with remarkable accuracy. The model classifies eight primary cancer types and 26 subtypes, each represented by 5,000 images. Our approach combines various computational tools, including pre-trained Convolutional Neural Networks, Machine learning, and Deep learning classifiers such as KNN and SVM, and innovative multimodal architectures of merged CNN-LSTM hybrids. We applied two distinct classification strategies. In our first approach, the main class and subclass are classified together. In the second approach, the model first predicts the main eight classes and then 26 subclasses concerning the main class classification, where the KNN model achieved higher accuracy for the Lymphoma class than CNNs. Finally, the X-OR gate-based fusion technique applied after prediction significantly reduces misclassifications and enhances the certainty of cancer types. Our findings reveal great accuracy levels of 99.25% for primary cancer classifications and 97.80% for subclass classifications. The introduction of novel models, Vception (VGG + Inception) and Vmobilnet (VGG + MobileNet), integrated with LSTM, further advances diagnostic capabilities. Again, By utilizing an X-OR gate post-prediction from Vmobilenet and Vception models, we achieved a main class accuracy of 99.95% and a subclass accuracy of 99.13%, significantly boosting model confidence. Moreover, individually, KNN achieved 97.14% accuracy for the Lymphoma class using PCA. This study not only sets a new benchmark in cancer detection but also promises to improve patient care and treatment outcomes significantly.

Keywords

• Cancer detection
• deep learning
• cancer imaging
• CNNs
• misclassifications
• PCA