Klasifikasi Jenis Biji Kopi Menggunkan Convolutional Neural Network dan Transfer Learning pada Model VGG16 dan MobileNetV2

Proses pengklasifikasian juga digunakan dalam artificial intelligence (AI), yang merupakan kecerdasan yang dibuat oleh komputer, sehingga dapat menirukan tindakan seperti halnya manusia pada umumnya dan dapat menangkap kejadian yang terjadi di lingkungan sekitarnya. Melihat perkembangan perdagangan kopi internasional yang sangat tinggi, dapat disimpulkan jika terdapat jenis kopi yang memiliki kualitas terbaiklah yang akan banyak dicari oleh negara pengimpor kopi. Terdapat beberapa jenis kopi diantaranya adalah kopi Arabica, kopi Robusta, kopi Liberica. Pada saat ini kopi sangat banyak di nikmati oleh masyarakat baik itu kalangan muda atau pun tua, dengan seiring berjalannya waktu pun peminat kopi terus meningkat. Melalui teknologi yang ada saat ini maka dapat dibedakan jenis biji kopi Robusta, Arabica, Liberica. Salah satu teknologi yang dapat digunakan adalah deep learning. Tujuan dari penelitian ini adalah mengusulkan model Convolutional Neural Network (CNN)-Transfer Learning untuk diimplementasikan pada sistem cerdas untuk proses klasifikasi citra jenis biji kopi. Metode yang digunakan dalam penelitian ini adalah model CNN transfer learning VGG16 dan MobileNetV2. Dari hasil pengujian yang dilakukan pada 3 model yakni model CNN, Model CNN-transfer learning VGG16 dan MobileNetV2 didapatkan hasil bahwa akurasi yang paling tinggi didapatkan ketika melakukan klasifikasi citra biji kopi dengan menggunakan CNN-transfer learning model MobileNetV2 yakni sebesar 96%. Tingkat akurasi yang meningkat jika dibandingkan dengan model CNN biasa mengindikasikan bahwa penggunaan transfer learning memberikan efek yang baik pada tingkat akurasi yang didapatkan. Kenaikan sebesar 1% memang tidak terlalu besar akan tetapi dengan adanya kenaikan tersebut membuka peluang untuk meningkatkan lebih tinggi menggunakan model transfer learning lainnya

Survey on Therapy Prediction using Deep Learning for Pores and Skin Diseases

Introduction: Prediction and detection of skin ailments have generally been a hard and important task for health care specialists.  In the cutting-edge situation majority of the pores and skin care practitioners are the uses of traditional techniques to diagnose the ailment which may also take a large amount of time. Skin Diseases are excessive troubles in recent times as it is a consider form of environmental factors, socioeconomic elements, loss of entire weight loss program, and so on. Identifying the particular skin disease by computer vision is introduced as a novel task. Based on skin or pore disease, certain therapy can be suggested. In proposed study there are different applications based on deep learning are studied with computer vision task for better performance of proposed application. Famous deep learning algorithms may include CNN (convolutional neural network) , RNN (Recurrent Neural network), etc. Objective: To diagnose skin disease with dermoscopic images automatically. Developing automated strategies to improve the accuracy of analysis for multiple psoriasis and skin diseases Methods: In existing techniques many machine learning models are used which is having high complexity and require more time for analysis. So, in this study different deep learning models are studied for understanding performance difference between different models. This paper is a comparative check about skin illnesses related to ordinary skin issues in addition to cosmetology. Image selection, segmentation of skin disease detection and classification are the important steps can be used for oily, dry, and ordinary pores. Result: The field of dermatology has seen promising results from studies on various Convolutional Neural Network (CNN) algorithms for classifying skin diseases based on clinical images. These studies have concentrated on utilizing the strength of deep learning and computer vision techniques to classify and diagnose different skin conditions using facial images precisely. Conclusion: A survey of numerous papers is achieved on basis of technologies used, outcomes with accuracy, moral behavior, and number of illnesses diagnosed, datasets. Different existing research methodologies are compared with present deep learning architectures for understanding superior performance of deep learning models. Using deep learning, we can predict pore and skin diseases. In proposed study, introduction to different algorithms of deep learning which are combined with computer vision tasks to find the skin disease and pore disease are studied. Therapy can be predicted based on type of skin or pore disease

Facial Skin Disease Detection using Image Processing

Busy lifestyle, modernization, increasing pollution and unhealthy diet have led to problems which people are neglecting. Not drinking enough water, stress and hormonal changes are causing problems to skin. Causes may be situational or genetic. Few skin conditions are minor while others can be life-threatening. The skin is the largest organ of the body and is composed of water, proteins, fats and minerals. Problems appear on outer layer of the skin that is epidermis. Skin diseases are considered to be the fourth most common cause of human illness. Skin diseases are observed to increase with age and were seen frequently in both men and women. Skin disorders can be temporary or permanent. Skin diseases have an impact on individual, family and social life caused by inadequate self-treatment which may also induce psychological problems. In recent years, use of computer technologies is becoming practically universal for both personal and professional issues. Facial skin problem identification and recognition has evolved to a great extent over the years. Detection of skin diseases is done using Convolution Neural Network (CNN) and image processing methods. CNN yields better performance in terms of accuracy, precision and results than the existing conventional methods. Image processing uses digital computer to process the images through an algorithm. We focus on features like skin tone, skin texture and color. We present a brief review about various facial skin problems providing more insight about the effective models and algorithms used

Exploring the Potential of Convolutional Neural Networks in Healthcare Engineering for Skin Disease Identification

Skin disorders affect millions of individuals worldwide, underscoring the urgency of swift and accurate detection for optimal treatment outcomes. Convolutional Neural Networks (CNNs) have emerged as valuable assets for automating the identification of skin ailments. This paper conducts an exhaustive examination of the latest advancements in CNN-driven skin condition detection. Within dermatological applications, CNNs proficiently analyze intricate visual motifs and extricate distinctive features from skin imaging datasets. By undergoing training on extensive data repositories, CNNs proficiently classify an array of skin maladies such as melanoma, psoriasis, eczema, and acne. The paper spotlights pivotal progressions in CNN-centered skin ailment diagnosis, encompassing diverse CNN architectures, refinement methodologies, and data augmentation tactics. Moreover, the integration of transfer learning and ensemble approaches has further amplified the efficacy of CNN models. Despite their substantial potential, there exist pertinent challenges. The comprehensive portrayal of skin afflictions and the mitigation of biases mandate access to extensive and varied data pools. The quest for comprehending the decision-making processes propelling CNN models remains an ongoing endeavor. Ethical quandaries like algorithmic predisposition and data privacy also warrant significant consideration. By meticulously scrutinizing the evolutions, obstacles, and potential of CNN-oriented skin disorder diagnosis, this critique provides invaluable insights to researchers and medical professionals. It underscores the importance of precise and efficacious diagnostic instruments in ameliorating patient outcomes and curbing healthcare expenditures

Detección de carcinoma basocelular utilizando red neuronal convolucional y Support Vector Machine

El cáncer de piel es uno de los tipos de cáncer más frecuente en los seres humanos, abarca cerca de un tercio total de las neoplasias. Dentro del cáncer de piel encontramos al carcinoma basocelular (CBC) siendo este el tipo de cáncer más frecuente a nivel mundial. Una serie de estudios que involucran enfoques de aprendizaje profundo ya se han desempeñado en un número considerable como la clasificación de imágenes. Los modelos utilizados en dichas tareas emplean la función Softmax (modelo clásico) en la capa de clasificación. Sin embargo, se han realizado estudios que utilizan una alternativa a la función Softmax para la clasificación: la máquina de vectores de soporte (SVM). El uso de SVM en una arquitectura de red neuronal artificial produce resultados relativamente mejores que el uso de la función Softmax convencional. Por este motivo se construyó un sistema que diagnostica el carcinoma basocelular implementando un modelo híbrido de red neuronal convolucional y máquina de vectores de soporte para clasificar el CBC. Los resultados obtenidos fueron medidos con las métricas de precisión, recall, f1-score y exactitud obteniendo 94.51%, 88.42%, 91.36% y 91.54% respectivamente

The Effectiveness of Transfer Learning Systems on Medical Images

Deep neural networks have revolutionized the performances of many machine learning tasks such as medical image classification and segmentation. Current deep learning (DL) algorithms, specifically convolutional neural networks are increasingly becoming the methodological choice for most medical image analysis. However, training these deep neural networks requires high computational resources and very large amounts of labeled data which is often expensive and laborious. Meanwhile, recent studies have shown the transfer learning (TL) paradigm as an attractive choice in providing promising solutions to challenges of shortage in the availability of labeled medical images. Accordingly, TL enables us to leverage the knowledge learned from related data to solve a new problem. The objective of this dissertation is to examine the effectiveness of TL systems on medical images. First, a comprehensive systematic literature review was performed to provide an up-to-date status of TL systems on medical images. Specifically, we proposed a novel conceptual framework to organize the review. Second, a novel DL network was pretrained on natural images and utilized to evaluate the effectiveness of TL on a very large medical image dataset, specifically Chest X-rays images. Lastly, domain adaptation using an autoencoder was evaluated on the medical image dataset and the results confirmed the effectiveness of TL through fine-tuning strategies. We make several contributions to TL systems on medical image analysis: Firstly, we present a novel survey of TL on medical images and propose a new conceptual framework to organize the findings. Secondly, we propose a novel DL architecture to improve learned representations of medical images while mitigating the problem of vanishing gradients. Additionally, we identified the optimal cut-off layer (OCL) that provided the best model performance. We found that the higher layers in the proposed deep model give a better feature representation of our medical image task. Finally, we analyzed the effect of domain adaptation by fine-tuning an autoencoder on our medical images and provide theoretical contributions on the application of the transductive TL approach. The contributions herein reveal several research gaps to motivate future research and contribute to the body of literature in this active research area of TL systems on medical image analysis