Rethinking Breast Cancer Diagnosis through Deep Learning Based Image Recognition

Application of image recognition technology in pathological diagnosis of blood smears

Traditional manual blood smear diagnosis methods are time-consuming and prone to errors, often relying heavily on the experience of clinical laboratory analysts for accuracy. As breakthroughs in key technologies such as neural networks and deep learning continue to drive digital transformation in the medical field, image recognition technology is increasingly being leveraged to enhance existing medical processes. In recent years, advancements in computer technology have led to improved efficiency in the identification of blood cells in blood smears through the use of image recognition technology. This paper provides a comprehensive summary of the methods and steps involved in utilizing image recognition algorithms for diagnosing diseases in blood smears, with a focus on malaria and leukemia. Furthermore, it offers a forward-looking research direction for the development of a comprehensive blood cell pathological detection system.

DI-UNet: dual-branch interactive U-Net for skin cancer image segmentation

PURPOSE

Skin disease is a prevalent type of physical ailment that can manifest in multitude of forms. Many internal diseases can be directly reflected on the skin, and if left unattended, skin diseases can potentially develop into skin cancer. Accurate and effective segmentation of skin lesions, especially melanoma, is critical for early detection and diagnosis of skin cancer. However, the complex color variations, boundary ambiguity, and scale variations in skin lesion regions present significant challenges for precise segmentation.

METHODS

We propose a novel approach for melanoma segmentation using a dual-branch interactive U-Net architecture. Two distinct sampling strategies are simultaneously integrated into the network, creating a vertical dual-branch structure. Meanwhile, we introduce a novel dual-channel symmetrical convolution block (DCS-Conv), which employs a symmetrical design, enabling the network to exhibit a horizontal dual-branch structure. The combination of the vertical and horizontal distribution of the dual-branch structure enhances both the depth and width of the network, providing greater diversity and rich multiscale cascade features. Additionally, this paper introduces a novel module called the residual fuse-and-select module (RFS module), which leverages self-attention mechanisms to focus on the specific skin cancer features and reduce irrelevant artifacts, further improving the segmentation accuracy.

RESULTS

We evaluated our approach on two publicly skin cancer datasets, ISIC2016 and PH2, and achieved state-of-the-art results, surpassing previous outcomes in terms of segmentation accuracy and overall performance.

CONCLUSION

Our proposed approach holds tremendous potential to aid dermatologists in clinical decision-making.

Breast Cancer Classification Using Synthesized Deep Learning Model with Metaheuristic Optimization Algorithm

Breast cancer is the second leading cause of mortality among women. Early and accurate detection plays a crucial role in lowering its mortality rate. Timely detection and classification of breast cancer enable the most effective treatment. Convolutional neural networks (CNNs) have significantly improved the accuracy of tumor detection and classification in medical imaging compared to traditional methods. This study proposes a comprehensive classification technique for identifying breast cancer, utilizing a synthesized CNN, an enhanced optimization algorithm, and transfer learning. The primary goal is to assist radiologists in rapidly identifying anomalies. To overcome inherent limitations, we modified the Ant Colony Optimization (ACO) technique with opposition-based learning (OBL). The Enhanced Ant Colony Optimization (EACO) methodology was then employed to determine the optimal hyperparameter values for the CNN architecture. Our proposed framework combines the Residual Network-101 (ResNet101) CNN architecture with the EACO algorithm, resulting in a new model dubbed EACO-ResNet101. Experimental analysis was conducted on the MIAS and DDSM (CBIS-DDSM) mammographic datasets. Compared to conventional methods, our proposed model achieved an impressive accuracy of 98.63%, sensitivity of 98.76%, and specificity of 98.89% on the CBIS-DDSM dataset. On the MIAS dataset, the proposed model achieved a classification accuracy of 99.15%, a sensitivity of 97.86%, and a specificity of 98.88%. These results demonstrate the superiority of the proposed EACO-ResNet101 over current methodologies.