Early and accurate cancer detection continues to rely heavily on advances in artificial intelligence, particularly deep learning and machine-learning systems designed for multimodal imaging and clinical data analysis. Recent studies in breast cancer diagnosis highlight the growing efficiency of lightweight CNN architectures such as MobileNetV3, Xception, and hybrid CNN–SVM models in classifying ultrasound and histopathological images, with several approaches demonstrating high accuracy and robustness despite limited and variable datasets. Parallel developments in liver cancer research show significant improvements through CNN-based CT segmentation frameworks, multiparametric ultrasound techniques, and hybrid optimization-driven classifiers, all enabling better lesion characterization and early disease detection. Similarly, thyroid cancer diagnostics benefit from enhanced YOLO-based detectors, attention-guided CNNs, and classical ML models, which collectively improve nodule classification using ultrasound or biopsy images. Additionally, multi-cancer prediction frameworks using MRI and structured clinical datasets show the promising role of SVM and ensemble learning in generalized cancer risk assessment. Across all modalities, these studies emphasize the importance of integrating deep learning with IoT infrastructure, multi-source transfer learning, and optimization algorithms to address noise, data imbalance, and limited imaging quality. Together, the reviewed works demonstrate how multimodal AI-driven systems are driving a shift towards faster, more reliable and resource-efficient cancer detection technologies, ultimately supporting early diagnosis and broader accessibility in clinical and underserved settings.

In this review, we examined a wide range of multimodal cancer detection approaches used for breast, liver, and thyroid cancer diagnosis. The studies covered diverse imaging modalities—including histopathology, ultrasound, CT, MRI, and biopsy images—along with deep learning, traditional machine-learning models, hybrid frameworks, and optimization-based classifiers. Each method demonstrated distinct strengths in feature extraction, lesion classification, segmentation accuracy, and computational efficiency. This review also summarized the different algorithms, performance metrics, preprocessing strategies, and architectures used across the literature, highlighting how advancements such as lightweight CNNs, transfer learning, hybrid CNN–SVM systems, and multiparametric imaging have contributed to improved early detection outcomes. Despite these improvements, several challenges persist, particularly related to dataset imbalance, imaging noise, modality-specific variability, and limited real-time deployment in resource-constrained settings. A key research gap identified in this work is the absence of a unified, generalized diagnostic model capable of accepting any type of medical image and reliably predicting any cancer type. Current systems remain highly specialized and restricted to specific organs or imaging modalities, limiting their scalability and clinical applicability. This gap opens opportunities for future research to explore universal, multimodal frameworks integrating cross-domain learning, multi-task architectures, and large-scale standardized datasets. Overall, this review provides valuable insights into existing multimodal cancer detection technologies while emphasizing the need for more holistic, interoperable, and clinically adaptable AI solutions for early cancer diagnosis.

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