Yes / People start posting tweets containing texts, images, and videos as soon as a disaster hits an area. The analysis of these disaster-related tweet texts, images, and videos can help humanitarian response organizations in better decision-making and prioritizing their tasks. Finding the informative contents which can help in decision making out of the massive volume of Twitter content is a difficult task and require a system to filter out the informative contents. In this paper, we present a multi-modal approach to identify disaster-related informative content from the Twitter streams using text and images together. Our approach is based on long-short-term-memory (LSTM) and VGG-16 networks that show significant improvement in the performance, as evident from the validation result on seven different disaster-related datasets. The range of F1-score varied from 0.74 to 0.93 when tweet texts and images used together, whereas, in the case of only tweet text, it varies from 0.61 to 0.92. From this result, it is evident that the proposed multi-modal system is performing significantly well in identifying disaster-related informative social media contents.

The use of AI in the medical field is becoming more widespread, and research on its various applications is very popular. In biomedical image analysis, Convolutional Neural Networks (CNN), which are specialized in image processing, can analyze X-rays and detect signs of different diseases. However, to achieve that, CNNs require vast amounts of X-ray images with labels specifying the disease (labeled training data), which is not always available. One method to overcome this obstacle is the use of data augmentation. Data augmentation is manipulating images through flipping, rotating, or changing the saturation or brightness, among other methods. The purpose is to increase and diversify the training data to make the CNN model more robust. Our study aims to investigate the effects of different data augmentation techniques on the performance of a CNN model in detecting Pneumothorax. After fine-tuning our CNN model’s hyper-parameters, three data augmentation methods (color, geometric, and noise) and their combinations were applied to our model. We then tested and compared the effects of each data augmentation method on the accuracy of our model. Our study concluded that color augmentation performed the best compared to the other augmentation methods, while geometric augmentation had the worst performance. However, none of the augmentation methods significantly improved the original model’s performance, which can be attributed to the model’s configuration of hyper-parameters, leaving no room for improvement. / Användningen av AI inom det medicinska området blir mer utbredd och forskning om dess olika tillämpningar är mycket populär. Inom biomedicinsk bildanalys kan Convolutional Neural Networks (CNN), som är specialiserade på bildbehandling, analysera röntgenstrålar och upptäcka tecken på olika sjukdomar. Men för att uppnå det kräver CNN stora mängder röntgenbilder med etiketter som anger sjukdomen (märkta träningsdata), vilket inte alltid är tillgängligt. En metod för att övervinna detta hinder är användningen av dataförstärkning. Dataförstärkning är att manipulera bilder genom att bläddra, rotera eller ändra mättnad eller ljusstyrka, bland andra metoder. Syftet är att öka och diversifiera träningsdata för att göra CNN-modellen mer robust. Vår studie syftar till att undersöka effekterna av olika dataförstärkningstekniker på prestandan hos en CNN-modell vid detektering av pneumothorax. Efter att ha finjusterat vår CNN-modells hyperparametrar, tillämpades tre dataförstärkningsmetoder (färg, geometrisk och brus) och deras kombinationer på vår modell. Vi testade och jämförde sedan effekterna av varje dataförstärkningsmetod på noggrannheten i vår modell. Vår studie drog slutsatsen att färgförstärkning presterade bäst jämfört med andra förstärkningsmetoder, medan geometrisk förstärkning hade sämst prestanda. Ingen av förstärkningsmetoderna förbättrade dock den ursprungliga modellens prestanda avsevärt, vilket kan tillskrivas modellens konfiguration av hyperparametrar, vilket inte lämnar något utrymme för förbättringar.

Melanoma is a life threatening type of skin cancer with numerous fatal incidences all over the world. The 5-year survival rate is very high for cases that are diagnosed in early stage. So, early detection of melanoma is of vital importance. Except for several techniques that clinicians apply so as to improve the reliability of detecting melanoma, many automated algorithms and mobile applications have been developed for the same purpose.In this paper, deep learning model designed from scratch as well as the pretrained models Inception v3 and VGG-16 are used with the aim of developing a reliable tool that can be used for melanoma detection by clinicians and individual users. Dermatologists who use dermoscopes can take advantage of the algorithms trained on dermoscopical images and acquire a confirmation about their diagnosis. On the other hand, the models trained on clinical images can be used on mobile applications, since a cell phone camera takes images similar to them.The results using Inception v3 model for dermoscopical images achieved accuracy 91.4%, sensitivity 87.8% and specificity 92.3%. For clinical images, the VGG-16 model achieved accuracy 86.3%, sensitivity 84.5% and specificity 88.8%. The results are compared to those of clinicians, which shows that the algorithms can be used reliably for the detection of melanoma.