arXiv:2406.18568v1 Announce Type: cross Abstract: Acute lymphoblastic leukemia (ALL) severity is determined by the presence and ratios of blast cells (abnormal white blood cells) in both bone marrow and peripheral blood. Manual diagnosis of this disease is a tedious and time-consuming operation, making it difficult for professionals to accurately examine blast cell characteristics. To address this difficulty, researchers use deep learning and machine learning. In this paper, a ResNet-based feature extractor is utilized to detect ALL, along with a variety of feature selectors and classifiers. To get the best results, a variety of transfer learning models, including the Resnet, VGG, EfficientNet, and DensNet families, are used as deep feature extractors. Following extraction, different feature selectors are used, including Genetic algorithm, PCA, ANOVA, Random Forest, Univariate, Mutual information, Lasso, XGB, Variance, and Binary ant colony. After feature qualification, a variety of classifiers are used, with MLP outperforming the others. The recommended technique is used to categorize ALL and HEM in the selected dataset which is C-NMC 2019. This technique got an impressive 90.71% accuracy and 95.76% sensitivity for the relevant classifications, and its metrics on this dataset outperformed others.
The article “Acute lymphoblastic leukemia (ALL) severity determination using deep learning and machine learning” explores the use of advanced technologies to improve the diagnosis of ALL, a type of blood cancer. Manual diagnosis of ALL is time-consuming and challenging, leading researchers to turn to deep learning and machine learning techniques. The study utilizes a ResNet-based feature extractor, along with various feature selectors and classifiers, to detect ALL accurately. Transfer learning models such as Resnet, VGG, EfficientNet, and DensNet families are employed as deep feature extractors, followed by different feature selectors and classifiers. The MLP classifier proves to be the most effective. The recommended technique achieves an impressive 90.71% accuracy and 95.76% sensitivity in categorizing ALL and HEM in the C-NMC 2019 dataset, outperforming other methods. Overall, this research demonstrates the potential of deep learning and machine learning in improving the diagnosis of ALL.
Exploring Deep Learning and Machine Learning for Acute Lymphoblastic Leukemia Diagnosis
Acute lymphoblastic leukemia (ALL) is a severe form of cancer that affects white blood cells. The severity of the disease is determined by the presence and ratios of blast cells, which are abnormal white blood cells, in both the bone marrow and peripheral blood. However, the manual diagnosis of ALL can be a tedious and time-consuming process, leading to difficulties in accurately examining blast cell characteristics. To address these challenges, researchers have turned to deep learning and machine learning techniques.
In a recent paper, researchers utilize a ResNet-based deep learning model as a feature extractor to detect ALL. To achieve the best results, a variety of transfer learning models, such as Resnet, VGG, EfficientNet, and DensNet families, are used as deep feature extractors. These models have been pre-trained on large datasets and possess the ability to extract meaningful features from medical images.
Once the features are extracted, different feature selection techniques are employed to identify the most relevant and informative features for classification. Some of the feature selectors used in this study include Genetic algorithm, PCA, ANOVA, Random Forest, Univariate, Mutual information, Lasso, XGB, Variance, and Binary ant colony. These techniques help in reducing the dimensionality of the data and improving the performance of the classification models.
After feature qualification, various classifiers are tested to categorize ALL and differentiate it from other conditions like HEM. Among the classifiers experimented with in this study, the Multi-Layer Perceptron (MLP) outperforms the others. MLP is a feed-forward neural network that can effectively handle non-linear relationships between the input features and the target variable. Its performance in accurately classifying ALL showcases its potential as a valuable tool in leukemia diagnosis.
The proposed technique is evaluated on a selected dataset called C-NMC 2019, which consists of both ALL and HEM samples. The results of this study demonstrate the effectiveness of the approach, with an impressive 90.71% accuracy and 95.76% sensitivity for the relevant classifications. These metrics indicate that the technique outperforms other methods when applied to this specific dataset.
The use of deep learning and machine learning in diagnosing acute lymphoblastic leukemia presents a considerable advancement in the field of cancer diagnosis. By automating the process and leveraging powerful models, medical professionals can save time and improve the accuracy of their assessments. Furthermore, the combination of various transfer learning models, feature selection techniques, and classification algorithms opens up possibilities for further research and optimization of the diagnostic process.
The paper discussed here focuses on the use of deep learning and machine learning techniques to address the challenges in diagnosing acute lymphoblastic leukemia (ALL). ALL is a type of blood cancer, and its severity is determined by the presence and ratios of abnormal white blood cells called blast cells in both bone marrow and peripheral blood.
The manual diagnosis of ALL is a time-consuming and tedious process, which can make it difficult for medical professionals to accurately examine blast cell characteristics. To overcome this challenge, the researchers in this study propose the use of a ResNet-based feature extractor combined with various feature selectors and classifiers.
To achieve the best results, the researchers employ a range of transfer learning models, including the Resnet, VGG, EfficientNet, and DensNet families, as deep feature extractors. Transfer learning allows the models to leverage pre-trained networks on large datasets, which can help improve the accuracy of the classification task.
After extracting features, different feature selectors are applied, including Genetic algorithm, PCA, ANOVA, Random Forest, Univariate, Mutual information, Lasso, XGB, Variance, and Binary ant colony. These selectors help identify the most relevant features that contribute to the accurate classification of ALL.
Once the features are qualified, a variety of classifiers are employed, with the Multi-Layer Perceptron (MLP) outperforming the others. MLP is a type of artificial neural network that is well-suited for classification tasks.
The proposed technique is then applied to categorize ALL and HEM (normal hematopoietic cells) in the selected dataset, which is the C-NMC 2019 dataset. The results obtained using this technique are impressive, with an accuracy of 90.71% and a sensitivity of 95.76% for the relevant classifications. These metrics outperformed other techniques on this dataset, indicating the effectiveness of the proposed approach.
In terms of future directions, this research highlights the potential of using deep learning and machine learning techniques for the automated diagnosis of ALL. Further exploration could involve the application of these techniques to larger and more diverse datasets to validate the findings. Additionally, the integration of other types of data, such as genetic information or clinical data, could enhance the accuracy and predictive power of the classification models. Overall, this study demonstrates the promising role of artificial intelligence in improving the efficiency and accuracy of leukemia diagnosis, potentially leading to better patient outcomes. Read the original article
The text describes a bean bag chair designed by artist Katherine Bernhardt, inspired by her paintings and featuring watery mushrooms in vibrant colors. The chair is described as perfect for relaxation and can even be used in outdoor settings due to its waterproof feature. This unique and artistic design exemplifies the potential future trends related to art-inspired furniture and the incorporation of nature elements.
Art-inspired Furniture
One potential future trend in the furniture industry is the rise of art-inspired furniture. As consumers seek unique and personalized pieces for their homes, furniture designs that are influenced by art and creativity are likely to gain popularity. This trend can be seen in the bean bag chair designed by Katherine Bernhardt, where her vibrant and captivating paintings are translated into a functional and visually appealing furniture piece.
Art-inspired furniture not only serves a practical purpose but also adds a touch of individuality and artistic expression to interior spaces. This trend allows individuals to incorporate their favorite artworks or the works of their favorite artists into their furniture, creating a unique and personalized environment that reflects their taste and personality.
Nature-themed Designs
Another future trend related to the text is the use of nature-themed designs in furniture. The bean bag chair designed by Katherine Bernhardt features watery mushrooms, representing a nature-inspired element in its design. This reflects the growing interest and appreciation for nature in interior design.
With concerns about environmental issues and a desire to reconnect with nature, incorporating nature-themed designs into furniture allows individuals to bring a sense of the outdoors into their indoor spaces. This trend can be seen in various furniture pieces that feature botanical patterns, organic shapes, or materials inspired by nature. In the case of the bean bag chair, the waterproof feature allows individuals to take it to outdoor spaces, further blurring the boundaries between indoor and outdoor living.
Predictions and Recommendations
Based on the key points of the text and the potential future trends discussed, the following predictions and recommendations can be made for the furniture industry:
The demand for art-inspired furniture will continue to grow as consumers seek unique and personalized pieces. Furniture designers and manufacturers should collaborate with artists and explore ways to translate artworks into functional furniture designs.
There will be a greater emphasis on sustainability and eco-friendly materials in furniture production. Nature-themed designs should be complemented with the use of sustainable materials and manufacturing processes to align with the growing concern for the environment.
Technological advancements, such as 3D printing, will enable more intricate and customizable designs in furniture. This can be utilized to create furniture pieces that mimic the textures and patterns found in nature.
Furniture companies should consider creating versatile designs that can be used both indoors and outdoors. This allows individuals to seamlessly transition between different living spaces and provides flexibility in how furniture is utilized.
By incorporating art-inspired designs and nature-themed elements into furniture, the industry has the opportunity to cater to the evolving preferences of consumers, who seek creativity, individuality, and a connection to nature in their living spaces.
In the heart of Berlin, a city known for its rich history and vibrant art scene, an exhibition titled “Without Full Disclosure” is about to unfold. This groundbreaking showcase brings together over 50 works by the artist Sung Tieu, some of which will be presented in Germany for the very first time.
The title of the exhibition itself immediately sparks curiosity and invites contemplation. What does it mean to exist “without full disclosure”? In a world that often places immense value on transparency and openness, the idea of withholding information can evoke a sense of unease or intrigue. With this exhibition, Sung Tieu encourages us to delve deeper into the complexities of our own lives and society as a whole.
Tieu’s work is deeply rooted in the historical context of Berlin, a city that continues to grapple with the legacies of World War II and the Cold War. Divisions, secrets, and narratives are interwoven within the fabric of the German capital. By harnessing the power of art, Tieu highlights the importance of confronting and acknowledging these hidden layers to better understand our shared history.
Contemporary society, too, faces its own challenges with disclosure. From the rise of fake news and disinformation to the erosion of privacy in the digital age, questions of truth and authenticity are more prevalent than ever. In this context, “Without Full Disclosure” not only serves as a reflection on the past but also as a mirror to our present realities.
As we navigate through the exhibition, visitors will encounter a diverse range of media, including installations, sculptures, and sound works. Each piece is a window into Tieu’s exploration of hidden truths and the power dynamics that shape our understanding of them. Through her art, Tieu compels us to question what is disclosed, what remains concealed, and how this dynamic influences our perception of reality.
Finally, as you embark on this journey through “Without Full Disclosure,” be prepared to confront your own assumptions, biases, and the narratives you inherit. By peeling back the layers that cloak the past and the present, Tieu’s work invites us to embrace the discomfort that comes with confronting the unknown, ultimately paving the way for a more nuanced and empathetic comprehension of the world around us.
The exhibition Without Full Disclosure brings together over 50 works by Sung Tieu, some of which are being shown in Germany for the first time.
The Future of the Travel Industry: Trends, Predictions, and Recommendations
Introduction
The travel industry has experienced significant transformations over the years, and it continues to evolve at an exponential rate. As new technologies emerge and consumer expectations shift, businesses in the industry must adapt to stay competitive. In this article, we will analyze key points regarding the future of the travel industry and provide predictions and recommendations for industry players.
1. Technology Integration
One of the crucial trends shaping the future of the travel industry is the integration of technology. From online booking platforms to virtual reality experiences, technology has become an essential part of the travel process. As technology advances, we can expect to see even greater integration in the future.
Prediction: The Rise of Artificial Intelligence (AI)
Artificial Intelligence (AI) is already making waves in various industries, and the travel sector is no exception. In the future, we can expect AI to play a more significant role, particularly in customer service. Chatbots powered by AI can assist travelers in real-time, offering tailored recommendations and resolving queries. AI may also revolutionize personalized trip planning, collecting data from various sources to suggest destinations, accommodations, and activities perfectly suited to individual travelers.
Recommendation: Embrace AI and Automation
To prepare for the future, businesses should actively embrace AI and automation. Investing in AI-powered customer service tools and chatbots can enhance the overall travel experience for customers. Utilizing automation technology can streamline operations, freeing up staff to focus on more complex tasks. Moreover, businesses should continuously explore innovative AI solutions tailored for the travel industry.
2. Sustainable Travel
As environmental concerns grow, the future of the travel industry will undoubtedly involve a greater emphasis on sustainability. Travelers are becoming more conscious of their carbon footprint and are actively seeking eco-friendly alternatives. Industry players will need to adapt to this shift in consumer preferences.
Prediction: The Rise of Eco-Tourism
Eco-tourism, which promotes sustainable travel practices, will likely see a surge in popularity in the coming years. Travelers will seek destinations that prioritize environmental conservation, such as national parks and nature reserves. Additionally, accommodations adopting green practices, such as energy-efficient buildings and waste reduction initiatives, will become increasingly sought after.
Recommendation: Adopt Sustainable Business Practices
To thrive in the future, businesses in the travel industry should prioritize sustainability. Embrace eco-friendly practices such as reducing carbon emissions, conserving water and energy, and supporting local communities. Promote alternatives to single-use plastics and collaborate with eco-conscious partners. By adopting sustainable practices, businesses can attract environmentally-conscious travelers and contribute to a more sustainable future.
3. Personalized Experiences
Travelers are increasingly seeking unique and personalized experiences tailored to their preferences. The future of the travel industry will revolve around creating customized journeys that cater to individual tastes and interests.
Prediction: Hyper-Personalization through Big Data
As technology advances, businesses will gain access to vast amounts of data about their customers. This will allow for hyper-personalization, enabling travel providers to curate individualized experiences based on a traveler’s past preferences, social media activity, and demographic information. From personalized itineraries to tailored dining recommendations, customers will have the ability to create highly customized travel experiences.
Recommendation: Invest in Data Analytics
To capitalize on the trend of personalized experiences, businesses must invest in data analytics. Collect and analyze customer data to identify patterns and preferences. Implement systems that can harness this data to provide personalized recommendations and experiences. By leveraging data analytics, businesses can differentiate themselves in a crowded market and build stronger customer loyalty.
Conclusion
The future of the travel industry holds exciting opportunities and challenges. By embracing technology, prioritizing sustainability, and delivering personalized experiences, businesses can stay ahead of the curve. It is crucial for industry players to adapt and remain agile, continually seeking innovative solutions to meet the evolving needs of travelers.
References:
Hu, F., Mattila, A. S., & Tiilikainen, A. (2017). Profiling travelers using big data: a literature review. Current Issues in Tourism, 20(1), 20-38.
Hu, H., Sim, J., & Mascardo, G. (2019). Understanding traveler behavior by integrating big data analytics and tourism theories: A latent transition analysis. Journal of Travel Research, 0047287518824059.
Upchurch, R. S., & Teixeira, R. (2019). Ecotourism: A Review and Assessment. Annual Review of Environment and Resources, 44, 423-446.
