Breast cancer is the most common cause of death in women around the world. A new tool has been adopted based on thermal imaging, deep convolutional networks, health applications on smartphones, and cloud computing for early detection of breast cancer. The development of the smart app included the use of Mastology Research with the Infrared Image DMR-IR database and the training of the modified version of deep convolutional neural network model inception V4 (MV4). In addition to designing the application in a graphical user interface and linking it with the AirDroid application to send thermal images from the smartphone to the cloud and to retrieve the suggestive diagnostic result from the cloud server to the smartphone. Moreover, to verify the proper operation of the app, a set of thermal images was sent from the smartphone to the cloud server from different distances and image acquisition procedures to verify the quality of the images. Four effects on the thermal image were applied: Blur, Shaken, Tilted, and Flipping were added to the images to verify the detection accuracy. After conducting repeated experiments, the classification results of early detection of breast cancer, generated from the MV4, illustrated high accuracy performance. The response time achieved after the successful transfer of diagnostic results from the smartphone to the cloud and back to the smartphone via the AirDroid application is six seconds. The results show that the quality of thermal images did not affect by different distances and methods except in one method when compressing thermal images by 5%, 15%, and 26%. The results indicate 1% as maximum detection accuracy when compressing thermal images by 5%, 15%, and 26%. In addition, the results indicate detection accuracy increased in Blurry images and Shaken images by 0.0002%, while diagnostic accuracy decreased to nearly 11% in Tilted images. Early detection of breast cancer using a thermal camera, deep convolutional neural network, cloud computing, and health applications of smartphones are valuable and reliable complementary tools for radiologists to reduce mortality rates.

In this study, we propose a framework that enhances breast cancer classification accuracy by preserving spatial features and leveraging in situ cooling support. The framework utilizes real-time thermography video streaming for early breast cancer detection using Deep Learning models. Inception v3, Inception v4, and a modified Inception Mv4 were developed using MATLAB 2019. However, the thermal camera was connected to a mobile phone to capture images of the breast area for classification of normal and abnormal breast. This study’s training dataset included 1000 thermal images, where a FLIR One Pro thermal camera connected to a mobile device was used for the imaging process. Of the 1000 images obtained, 700 images were considered for the normal breast thermography class while the 300 images were suitable for the abnormal class. We evaluate Deep Convolutional Neural Network models, such as Inception v3, Inception v4, and a modified Inception Mv4. Our results demonstrate that Inception Mv4, with real-time video streaming, efficiently detects even the slightest temperature contrast in breast tissue sequences achieving a 99.748% accuracy in comparison to a 99.712% and 96.8% for Inception v4 and v3, respectively. The use of in situ cooling gel further enhances image acquisition efficiency and detection accuracy. Interestingly, increasing the tumor surface temperature by 0.1% leads to an average 7% improvement in detection and classification accuracy. Our findings support the effectiveness of Inception Mv4 for real-time breast cancer detection, especially when combined with in situ cooling gel and varying tumor temperatures. In conclusion, future research directions should focus on incorporating thermal video clips into the thermal images database, utilizing high-quality thermal cameras, and exploring alternative Deep Learning models for improved breast cancer detection.

This article presents a numerical simulation model using COMSOL software to study breast thermophysical properties. It analyzes tumor heat at different locations within the breast, records breast surface temperatures, investigates the effects of factors such as blood perfusion, size, depth, and thermal conductivity on breast size, and applies Pennes’ bioheat formula to illustrate thermal distribution on the breast skin surface. An analysis was conducted to examine how changes in tumor location depth, size, metabolism, blood flow, and heat conductivity affect breast skin surface temperature. The simulation model results showed that the highest variations in skin temperatures for breasts with tumors and without tumors can range from 2.58 °C to 0.274 °C. Further, large breast size with a large surface area consistently reduces the temperature variations on the skin and might have difficulty in yielding observable temperature contrast. For small breast sizes, however, heat from tumor sizes below 0.5 cm might be quite difficult to detect, while tumors located deep within the breast layers could not produce observable temperature variations. Motivated by the above interesting results, an emulation experiment was conducted to enhance the observable heat and temperature background contrast, using situ-cooling gel applied to silicon breasts, while the tumor source was emulated using LEDs. The experiment was used to evaluate the effectiveness of adding situ-cooling to the breast surface area, and to study the modulated effect of tumor size and depth. Experimental results showed that situ-cooling enhances thermal contrast in breast thermal images. For example, for a tumor location at a depth of 10 cm, a difference of 6 °C can still be achieved with situ-cooling gel applied, a feat that was not possible in the simulation model. Furthermore, changes in tumor size and location depth significantly impacted surface temperature distribution.